Awesome-Machine-Learning-System-Papers

Awesome-Machine-Learning-System-Papers is a curated list of machine learning system papers in recent years (since 2017). Star this repository, and then you can keep abreast of the latest developments of this booming research field.

Thanks to all the people who made contributions to this project. We strongly encourage the researchers to make pull request (e.g., add missing papers, fix errors) and help the others in this community!

Conference

Currently, the listed papers are collected from the following conferences:

Conferences	2016	2017	2018	2019	2020	2021
OSDI	3	-	4	-	11	8
SOSP	-	1	-	10	-	3
SIGMOD	2	4	3	4	8	16
ASPLOS	-	3	3	9	4	5
ATC	-	2	3	5	8	8
PPoPP	-	2	4	2	1	15
HPCA	-	3	4	4	1	11
MICRO	-	4	6	9	13	4
SC	5	-	6	6	14	15
NSDI	-	3	-	3	-	4
ISCA	-	6	11	7	13	18
VLDB	3	5	6	-	4	3
SIGCOMM	-	-	-	3	2	2
ICDE	2	5	1	6	7	4
SIGKDD	-	5	4	7	8	8
EuroSys	2	1	4	5	3	5
SoCC	2	3	3	2	6	-
SysML	-	-	6	32	34	52
WWW	-	-	1
INFOCOM	-	2	4
SIGIR	-	-	-

Some conferences to be added in the future:

NeurIPS
ICML

Papers

OSDI

2016

TensorFlow: A System for Large-Scale Machine Learning OSDI 2016

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

Code: https://github.com/tensorflow/tensorflow
Exploring the Hidden Dimension in Graph Processing OSDI 2016

Mingxing Zhang, Yongwei Wu, Kang Chen, Xuehai Qian, Xue Li, Weimin Zheng
Gemini: A Computation-Centric Distributed Graph Processing System OSDI 2016

Xiaowei Zhu, Wenguang Chen, Weimin Zheng, Xiaosong Ma

Code: https://github.com/thu-pacman/GeminiGraph

2018

Ray: A Distributed Framework for Emerging AI Applications OSDI 2018

Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, Ion Stoica

Code: https://github.com/ray-project/ray
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning OSDI 2018

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Q. Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Code: https://github.com/apache/tvm
Gandiva: Introspective Cluster Scheduling for Deep Learning OSDI 2018

Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel, Xuan Peng, Hanyu Zhao, Quanlu Zhang, Fan Yang, Lidong Zhou
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems OSDI 2018

Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo Interlandi

2020

Serving DNNs like Clockwork: Performance Predictability from the Bottom Up OSDI 2020

Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, Jonathan Mace
A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters OSDI 2020

Yimin Jiang, Yibo Zhu, Chang Lan, Bairen Yi, Yong Cui, Chuanxiong Guo
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads OSDI 2020

Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia
PipeSwitch: Fast Pipelined Context Switching for Deep Learning Applications OSDI 2020

Zhihao Bai, Zhen Zhang, Yibo Zhu, Xin Jin

Code: https://github.com/netx-repo/PipeSwitch
HiveD: Sharing a GPU Cluster for Deep Learning with Guarantees OSDI 2020

Hanyu Zhao, Zhenhua Han, Zhi Yang, Quanlu Zhang, Fan Yang, Lidong Zhou, Mao Yang, Francis C. M. Lau, Yuqi Wang, Yifan Xiong, Bin Wang

Code: https://github.com/microsoft/hivedscheduler
AntMan: Dynamic Scaling on GPU Clusters for Deep Learning OSDI 2020

Wencong Xiao, Shiru Ren, Yong Li, Yang Zhang, Pengyang Hou, Zhi Li, Yihui Feng, Wei Lin, Yangqing Jia

Code: https://github.com/alibaba/GPU-scheduler-for-deep-learning
Ansor: Generating High-Performance Tensor Programs for Deep Learning OSDI 2020

Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica
Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks OSDI 2020

Lingxiao Ma, Zhiqiang Xie, Zhi Yang, Jilong Xue, Youshan Miao, Wei Cui, Wenxiang Hu, Fan Yang, Lintao Zhang, Lidong Zhou

Code: https://github.com/microsoft/nnfusion
A Tensor Compiler for Unified Machine Learning Prediction Serving OSDI 2020

Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi
Retiarii: A Deep Learning Exploratory-Training Framework OSDI 2020

Quanlu Zhang, Zhenhua Han, Fan Yang, Yuge Zhang, Zhe Liu, Mao Yang, Lidong Zhou
KungFu: Making Training in Distributed Machine Learning Adaptive

Luo Mai, Guo Li, Marcel Wagenländer, Konstantinos Fertakis, Andrei-Octavian Brabete, Peter R. Pietzuch

2021

Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning OSDI 2021

Aurick Qiao, Sang Keun Choe, Suhas Jayaram Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing

Code: https://github.com/petuum/adaptdl
Oort: Efficient Federated Learning via Guided Participant Selection OSDI 2021

Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, Mosharaf Chowdhury

Code: https://github.com/SymbioticLab/Oort
PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections OSDI 2021

Haojie Wang, Jidong Zhai, Mingyu Gao, Zixuan Ma, Shizhi Tang, Liyan Zheng, Yuanzhi Li, Kaiyuan Rong, Yuanyong Chen, Zhihao Jia

Code: https://github.com/thu-pacman/PET
Privacy Budget Scheduling OSDI 2021

Tao Luo, Mingen Pan, Pierre Tholoniat, Asaf Cidon, Roxana Geambasu, Mathias Lécuyer
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads OSDI 2021

John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, Guoqing Harry Xu

Code: https://github.com/uclasystem/dorylus
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs OSDI 2021

Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, Yufei Ding

Code: https://github.com/YukeWang96/OSDI21_AE
Marius: Learning Massive Graph Embeddings on a Single Machine OSDI 2021

Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman

Code: https://github.com/marius-team/marius
P3: Distributed Deep Graph Learning at Scale OSDI 2021

Swapnil Gandhi, Anand Padmanabha Iyer

SOSP

2017

DeepXplore: Automated Whitebox Testing of Deep Learning Systems SOSP 2017

Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana

Code: https://github.com/peikexin9/deepxplore

2019

PipeDream: Generalized Pipeline Parallelism for DNN Training SOSP 2019

Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia
A Generic Communication Scheduler for Distributed DNN Training Acceleration SOSP 2019

Yanghua Peng, Yibo Zhu, Yangrui Chen, Yixin Bao, Bairen Yi, Chang Lan, Chuan Wu, Chuanxiong Guo
Parity Models: Erasure-Coded Resilience for Prediction Serving Systems SOSP 2019

Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
TASO: Optimizing Deep Learning Computation with Automated Generation of Graph Substitutions SOSP 2019

Zhihao Jia, Oded Padon, James Thomas, Todd Warszawski, Matei Zaharia, Alex Aiken
Optimizing Data-Intensive Computations in Existing Libraries with Split Annotations SOSP 2019

Shoumik Palkar, Matei Zaharia
Niijima: Sound and Automated Computation Consolidation for Efficient Multilingual Data-Parallel Pipelines SOSP 2019

Guoqing Harry Xu, Margus Veanes, Michael Barnett, Madan Musuvathi, Todd Mytkowicz, Ben Zorn, Huan He, Haibo Lin
Nexus: A GPU Cluster Engine for Accelerating DNN-Based Video Analysis SOSP 2019

Haichen Shen, Lequn Chen, Yuchen Jin, Liangyu Zhao, Bingyu Kong, Matthai Philipose, Arvind Krishnamurthy, Ravi Sundaram
AutoMine: Harmonizing High-Level Abstraction and High Performance for Graph Mining SOSP 2019

Daniel Mawhirter, Bo Wu
KnightKing: A Fast Distributed Graph Random Walk Engine SOSP 2019

Ke Yang, MingXing Zhang, Kang Chen, Xiaosong Ma, Yang Bai, Yong Jiang

Code: https://github.com/KnightKingWalk/KnightKing
Gerenuk: Thin Computation over Big Native Data Using Speculative Program Transformation SOSP 2019

Christian Navasca, Cheng Cai, Khanh Nguyen, Brian Demsky, Shan Lu, Miryung Kim, Guoqing Harry Xu

2021

Gradient Compression Supercharged High-Performance Data Parallel DNN Training SOSP 2021

Youhui Bai, Cheng Li, Quan Zhou, Jun Yi, Ping Gong, Feng Yan, Ruichuan Chen, Yinlong Xu
Generating Complex, Realistic Cloud Workloads using Recurrent Neural Networks SOSP 2021 Shane Bergsma, Timothy Zeyl, Arik Senderovich, J. Christopher Beck
Random Walks on Huge Graphs at Cache Efficiency SOSP 2021

Ke Yang, Xiaosong Ma, Saravanan Thirumuruganathan, Kang Chen, Yongwei Wu

SIGMOD

2016

Building Machine Learning Systems that Understand SIGMOD 2016

Jeff Dean
M3: Scaling Up Machine Learning via Memory Mapping SIGMOD 2016

Dezhi Fang, Duen Horng Chau

2017

Heterogeneity-aware Distributed Parameter Servers SIGMOD 2017

Jiawei Jiang, Bin Cui, Ce Zhang, Lele Yu
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics SIGMOD 2017

Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton
Data Management in Machine Learning: Challenges, Techniques, and Systems SIGMOD 2017

Arun Kumar, Matthias Boehm, Jun Yang
Data Management Challenges in Production Machine Learning SIGMOD 2017

Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, Martin Zinkevich

2018

DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions SIGMOD 2018

Jiawei Jiang, Bin Cui, Ce Zhang, Fangcheng Fu
SketchML: Accelerating Distributed Machine Learning with Data Sketches SIGMOD 2018

Jiawei Jiang, Fangcheng Fu, Tong Yang, Bin Cui
Accelerating Machine Learning Inference with Probabilistic Predicates SIGMOD 2018

Yao Lu, Aakanksha Chowdhery, Srikanth Kandula, Surajit Chaudhuri

2019

PS2: Parameter Server on Spark SIGMOD 2019

Zhipeng Zhang, Bin Cui, Yingxia Shao, Lele Yu, Jiawei Jiang, Xupeng Miao
Large Scale Graph Mining with G-Miner SIGMOD 2019

Hongzhi Chen, Xiaoxi Wang, Chenghuan Huang, Juncheng Fang, Yifan Hou, Changji Li, James Cheng

Code: https://github.com/yaobaiwei/GMiner
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees SIGMOD 2019

Yongjoo Park, Jingyi Qing, Xiaoyang Shen, Barzan Mozafari
Democratizing Data Science through Interactive Curation of ML Pipelines SIGMOD 2019

Zeyuan Shang, Emanuel Zgraggen, Benedetto Buratti, Ferdinand Kossmann, Philipp Eichmann, Yeounoh Chung, Carsten Binnig, Eli Upfal, Tim Kraska

2020

Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs SIGMOD 2020

Yingxia Shao, Shiyue Huang, Xupeng Miao, Bin Cui, Lei Chen
TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines SIGMOD 2020

Emily Caveness, Paul Suganthan G. C., Zhuo Peng, Neoklis Polyzotis, Sudip Roy, Martin Zinkevich
Optimizing Machine Learning Workloads in Collaborative Environments SIGMOD 2020

Behrouz Derakhshan, Alireza Rezaei Mahdiraji, Ziawasch Abedjan, Tilmann Rabl, Volker Markl
Vertica-ML: Distributed Machine Learning in Vertica Database SIGMOD 2020

Arash Fard, Anh Le, George Larionov, Waqas Dhillon, Chuck Bear
DB4ML - An In-Memory Database Kernel with Machine Learning Support SIGMOD 2020

Matthias Jasny, Tobias Ziegler, Tim Kraska, Uwe Röhm, Carsten Binnig
Active Learning for ML Enhanced Database Systems SIGMOD 2020

Lin Ma, Bailu Ding, Sudipto Das, Adith Swaminathan
MemFlow: Memory-Aware Distributed Deep Learning SIGMOD 2020

Neil Band
Systems and ML: When the Sum is Greater than Its Parts SIGMOD 2020

Ion Stoica

2021

Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce SIGMOD 2021

Xupeng Miao, Xiaonan Nie, Yingxia Shao, Zhi Yang, Jiawei Jiang, Lingxiao Ma, Bin Cui
VF^2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning SIGMOD 2021

Fangcheng Fu, Yingxia Shao, Lele Yu, Jiawei Jiang, Huanran Xue, Yangyu Tao, Bin Cui
ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks SIGMOD 2021

Wentao Zhang, Yu Shen, Yang Li, Lei Chen, Zhi Yang, Bin Cui
Agile and Accurate CTR Prediction Model Training for Massive-Scale Online Advertising Systems SIGMOD 2021

Zhiqiang Xu, Dong Li, Weijie Zhao, Xing Shen, Tianbo Huang, Xiaoyun Li, Ping Li
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities SIGMOD 2021

Doris Xin, Hui Miao, Aditya G. Parameswaran, Neoklis Polyzotis
Vertex-Centric Visual Programming for Graph Neural Networks SIGMOD 2021

Yidi Wu, Yuntao Gui, Tatiana Jin, James Cheng, Xiao Yan, Peiqi Yin, Yufei Cai, Bo Tang, Fan Yu
Deep Learning: Systems and Responsibility SIGMOD 2021

Abdul Wasay, Subarna Chatterjee, Stratos Idreos
Expand your Training Limits! Generating Training Data for ML-based Data Management SIGMOD 2021

Francesco Ventura, Zoi Kaoudi, Jorge-Arnulfo Quiané-Ruiz, Volker Markl

Code: https://github.com/agora-ecosystem/data-farm
Towards Benchmarking Feature Type Inference for AutoML Platforms SIGMOD 2021

Vraj Shah, Jonathan Lacanlale, Premanand Kumar, Kevin Yang, Arun Kumar

Code: https://github.com/pvn25/ML-Data-Prep-Zoo/tree/master/MLFeatureTypeInference
LightNE: A Lightweight Graph Processing System for Network Embedding SIGMOD 2021

Jiezhong Qiu, Laxman Dhulipala, Jie Tang, Richard Peng, Chi Wang

Code: https://github.com/xptree/LightNE
Scalable and Usable Relational Learning With Automatic Language Bias SIGMOD 2021

Jose Picado, Arash Termehchy, Alan Fern, Sudhanshu Pathak, Praveen Ilango, John Davis

Code: https://github.com/OSU-IDEA-Lab/AutoBias
Model-Parallel Model Selection for Deep Learning Systems SIGMOD 2021

Kabir Nagrecha
Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra SIGMOD 2021

Shangyu Luo, Dimitrije Jankov, Binhang Yuan, Chris Jermaine
Automation of Data Prep, ML, and Data Science: New Cure or Snake Oil? SIGMOD 2021

Arun Kumar
Towards Demystifying Serverless Machine Learning Training SIGMOD 2021

Jiawei Jiang, Shaoduo Gan, Yue Liu, Fanlin Wang, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Ce Zhang

Code: https://github.com/DS3Lab/LambdaML
AI Meets Database: AI4DB and DB4AI SIGMOD 2021

Guoliang Li, Xuanhe Zhou, Lei Cao

ASPLOS

2017

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs ASPLOS 2017

Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu
SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing ASPLOS 2017

Ao Ren, Zhe Li, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Ji Li, Xuehai Qian, Bo Yuan
Optimizing CNNs on Multicores for Scalability, Performance and Goodput ASPLOS 2017

Samyam Rajbhandari, Yuxiong He, Olatunji Ruwase, Michael Carbin, Trishul M. Chilimbi

2018

Bridge the Gap Between Neural Networks and Neuromorphic Hardware with A Neural Network Compiler ASPLOS 2018

Yu Ji, Youhui Zhang, Wenguang Chen, Yuan Xie
MAERI: Enabling Flexible Dataflow Mapping over DNN Accelerators via Reconfigurable Interconnects ASPLOS 2018

Hyoukjun Kwon, Ananda Samajdar, Tushar Krishna
VIBNN: Hardware Acceleration of Bayesian Neural Networks ASPLOS 2018

Ruizhe Cai, Ao Ren, Ning Liu, Caiwen Ding, Luhao Wang, Xuehai Qian, Massoud Pedram, Yanzhi Wang

2019

PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference ASPLOS 2019

Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti, Geoffrey Ndu, Martin Foltin, R. Stanley Williams, Paolo Faraboschi, Wen-mei W. Hwu, John Paul Strachan, Kaushik Roy, Dejan S. Milojicic
FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture ASPLOS 2019

Yu Ji, Youyang Zhang, Xinfeng Xie, Shuangchen Li, Peiqi Wang, Xing Hu, Youhui Zhang, Yuan Xie
Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks ASPLOS 20189

Alberto Delmas Lascorz, Patrick Judd, Dylan Malone Stuart, Zissis Poulos, Mostafa Mahmoud, Sayeh Sharify, Milos Nikolic, Kevin Siu, Andreas Moshovos
TANGRAM: Optimized Coarse-Grained Dataflow for Scalable NN Accelerators ASPLOS 2019

Mingyu Gao, Xuan Yang, Jing Pu, Mark Horowitz, Christos Kozyrakis
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization ASPLOS 2019

H. T. Kung, Bradley McDanel, Sai Qian Zhang
Split-CNN: Splitting Window-based Operations in Convolutional Neural Networks for Memory System Optimization ASPLOS 2019

Tian Jin, Seokin Hong
HOP: Heterogeneity-Aware Decentralized Training ASPLOS 2019

Qinyi Luo, Jinkun Lin, Youwei Zhuo, Xuehai Qian
Astra: Exploiting Predictability to Optimize Deep Learning ASPLOS 2019

Muthian Sivathanu, Tapan Chugh, Sanjay S. Singapuram, Lidong Zhou
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Methods of Multipliers ASPLOS 2019

Ao Ren, Tianyun Zhang, Shaokai Ye, Jiayu Li, Wenyao Xu, Xuehai Qian, Xue Lin, Yanzhi Wang

Code: https://github.com/bowenl0218/bpgan-signal-compression

2020

Prague: High-Performance Heterogeneity-Aware Asynchronouos Decentralized Training ASPLOS 2020

Qinyi Luo, Jiaao He, Youwei Zhuo, Xuehai Qian
Capuchin: Tensor-based GPU Memory Management for Deep Learning ASPLOS 2020

Xuan Peng, Xuanhua Shi, Hulin Dai, Hai Jin, Weiliang Ma, Qian Xiong, Fan Yang, Xuehai Qian
DNNGuard: An Elastic Heterogeneous DNN Accelerator Architecture against Adversarial Attacks ASPLOS 2020

Xingbin Wang, Rui Hou, Boyan Zhao, Fengkai Yuan, Jun Zhang, Dan Meng, Xuehai Qian
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning ASPLOS 2020

Wei Niu, Xiaolong Ma, Sheng Lin, Shihao Wang, Xuehai Qian, Xue Lin, Yanzhi Wang, Bin Ren

2021

Neural architecture search as program transformation exploration ASPLOS 2021

Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle

Code: https://github.com/jack-willturner/nas-as-program-transformation-exploration
Analytical characterization and design space exploration for optimization of CNNs ASPLOS 2021

Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan

Code: https://github.com/HPCRL/ASPLOS_artifact
Mind mappings: enabling efficient algorithm-accelerator mapping space search ASPLOS 2021

Kartik Hegde, Po-An Tsai, Sitao Huang, Vikas Chandra, Angshuman Parashar, Christopher W. Fletcher

Code: https://github.com/kartik-hegde/mindMappings
NeuroEngine: a hardware-based event-driven simulation system for advanced brain-inspired computing ASPLOS 2021

Hunjun Lee, Chanmyeong Kim, Yujin Chung, Jangwoo Kim
Defensive approximation: securing CNNs using approximate computing ASPLOS 2021

Amira Guesmi, Ihsen Alouani, Khaled N. Khasawneh, Mouna Baklouti, Tarek Frikha, Mohamed Abid, Nael B. Abu-Ghazaleh

Code: https://github.com/AG-X09/Defensive-Approximation

ATC

2017

Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters ATC 2017

Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Eric P. Xing
Garaph: Efficient GPU-accelerated Graph Processing on a Single Machine with Balanced Replication ATC 2017

Lingxiao Ma, Zhi Yang, Han Chen, Jilong Xue, Yafei Dai

2018

Litz: Elastic Framework for High-Performance Distributed Machine Learning ATC 2018

Aurick Qiao, Abutalib Aghayev, Weiren Yu, Haoyang Chen, Qirong Ho, Garth A. Gibson, Eric P. Xing
Cavs: An Efficient Runtime System for Dynamic Neural Networks ATC 2018

Shizhen Xu, Hao Zhang, Graham Neubig, Wei Dai, Jin Kyu Kim, Zhijie Deng, Qirong Ho, Guangwen Yang, Eric P. Xing
DeepCPU: Serving RNN-based Deep Learning Models 10x Faster ATC 2018

Minjia Zhang, Samyam Rajbhandari, Wenhan Wang, Yuxiong He

2019

STRADS-AP: Simplifying Distributed Machine Learning Programming without Introducing a New Programming Model ATC 2019

Jin Kyu Kim, Abutalib Aghayev, Garth A. Gibson, Eric P. Xing
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs ATC 2019

Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, Yafei Dai
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads ATC 2019

Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang
Optimizing CNN Model Inference on CPUs ATC 2019

Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, Yida Wang
MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving ATC 2019

Chengliang Zhang, Minchen Yu, Wei Wang, Feng Yan

2020

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism ATC 2020

Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi
AutoSys: The Design and Operation of Learning-Augmented Systems ATC 2020

Chieh-Jan Mike Liang, Hui Xue, Mao Yang, Lidong Zhou, Lifei Zhu, Zhao Lucis Li, Zibo Wang, Qi Chen, Quanlu Zhang, Chuanjie Liu, Wenjun Dai
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training ATC 2020

Hongyu Zhu, Amar Phanishayee, Gennady Pekhimenko
ALERT: Accurate Learning for Energy and Timeliness

Chengcheng Wan, Muhammad Husni Santriaji, Eri Rogers, Henry Hoffmann, Michael Maire, Shan Lu
NeuOS: A Latency-Predictable Multi-Dimensional Optimization Framework for DNN-driven Autonomous Systems ATC 2020

Soroush Bateni, Cong Liu

Code: https://github.com/Soroosh129/NeuOS
PERCIVAL: Making In-Browser Perceptual Ad Blocking Practical with Deep Learning ATC 2020

Zain ul Abi Din, Panagiotis Tigas, Samuel T. King, Benjamin Livshits

Code: https://github.com/dxaen/percival
GraphWalker: An I/O-Efficient and Resource-Friendly Graph Analytic System for Fast and Scalable Random Walks ATC 2020

Rui Wang, Yongkun Li, Hong Xie, Yinlong Xu, John C. S. Lui
Scaph: Scalable GPU-Accelerated Graph Processing with Value-Driven Differential Scheduling ATC 2020

Long Zheng, Xianliang Li, Yaohui Zheng, Yu Huang, Xiaofei Liao, Hai Jin, Jingling Xue, Zhiyuan Shao, Qiang-Sheng Hua

2021

Zico: Efficient GPU Memory Sharing for Concurrent DNN Training ATC 2021

Gangmuk Lim, Jeongseob Ahn, Wencong Xiao, Youngjin Kwon, Myeongjae Jeon
Octo: INT8 Training with Loss-aware Compensation and Backward Quantization for Tiny On-device Learning ATC 2021

Qihua Zhou, Song Guo, Zhihao Qu, Jingcai Guo, Zhenda Xu, Jiewei Zhang, Tao Guo, Boyuan Luo, Jingren Zhou

Code: https://github.com/kimihe/Octo
GLIST: Towards In-Storage Graph Learning ATC 2021

Cangyuan Li, Ying Wang, Cheng Liu, Shengwen Liang, Huawei Li, Xiaowei Li
Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism ATC 2021

Saar Eliad, Ido Hakimi, Alon De Jagger, Mark Silberstein, Assaf Schuster

Code: https://github.com/saareliad/FTPipe
INFaaS: Automated Model-less Inference Serving ATC 2021

Francisco Romero, Qian Li, Neeraja J. Yadwadkar, Christos Kozyrakis

Code: https://github.com/stanford-mast/INFaaS
Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training ATC 2021

Geoffrey X. Yu, Yubo Gao, Pavel Golikov, Gennady Pekhimenko

Code: https://github.com/geoffxy/habitat
Refurbish Your Training Data: Reusing Partially Augmented Samples for Faster Deep Neural Network Training ATC 2021

Gyewon Lee, Irene Lee, Hyeonmin Ha, Kyung-Geun Lee, Hwarim Hyun, Ahnjae Shin, Byung-Gon Chun
ZeRO-Offload: Democratizing Billion-Scale Model Training ATC 2021

Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He

Code: https://github.com/microsoft/DeepSpeed

PPoPP

2017

Groute: An Asynchronous Multi-GPU Programming Model for Irregular Computations PPoPP 2017

Tal Ben-Nun, Michael Sutton, Sreepathi Pai, Keshav Pingali:

Code: https://github.com/groute/ppopp17-artifact
S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters PPoPP 2017

Ammar Ahmad Awan, Khaled Hamidouche, Jahanzeb Maqbool Hashmi, Dhabaleswar K. Panda

2018

Bridging the Gap between Deep Learning and Sparse Matrix Format Selection PPoPP 2018

Yue Zhao, Jiajia Li, Chunhua Liao, Xipeng Shen
LazyGraph: Lazy Data Coherency for Replicas in Distributed Graph-Parallel Computation PPoPP 2018

Lei Wang, Liangji Zhuang, Junhang Chen, Huimin Cui, Fang Lv, Ying Liu, Xiaobing Feng
Making Pull-Based Graph Processing Performant PPoPP 2018

Samuel Grossman, Heiner Litz, Christos Kozyrakis

Code: https://github.com/stanford-mast/Grazelle-PPoPP18
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks PPoPP 2018

Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska

2019

Beyond Human-Level Accuracy: Computational Challenges in Deep Learning PPoPP 2019

Joel Hestness, Newsha Ardalani, Gregory F. Diamos

Code: https://github.com/baidu-research/catamount
A Pattern Based Algorithmic Autotuner for Graph Processing on GPUs PPoPP 2019

Ke Meng, Jiajia Li, Guangming Tan, Ninghui Sun

Code: https://github.com/PAA-NCIC/gswitch

2020

Taming unbalanced training workloads in deep learning with partial collective operations PPoPP 2020

Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, Torsten Hoefler

2021

Understanding and bridging the gaps in current GNN performance optimizations PPoPP 2021

Kezhao Huang, Jidong Zhai, Zhen Zheng, Youngmin Yi, Xipeng Shen
Scaling implicit parallelism via dynamic control replication PPoPP 2021

Michael Bauer, Wonchan Lee, Elliott Slaughter, Zhihao Jia, Mario Di Renzo, Manolis Papadakis, Galen M. Shipman, Patrick S. McCormick, Michael Garland, Alex Aiken
Compiler support for near data computing PPoPP 2021

Mahmut Taylan Kandemir, Jihyun Ryoo, Xulong Tang, Mustafa Karaköy
BiPart: a parallel and deterministic hypergraph partitioner PPoPP 2021

Sepideh Maleki, Udit Agarwal, Martin Burtscher, Keshav Pingali

Code: https://github.com/IntelligentSoftwareSystems/Galois
GPTune: multitask learning for autotuning exascale applications PPoPP 2021

Yang Liu, Wissam M. Sid-Lakhdar, Osni Marques, Xinran Zhu, Chang Meng, James Weldon Demmel, Xiaoye S. Li

Code: https://github.com/gptune/GPTune
I/O lower bounds for auto-tuning of convolutions in CNNs PPoPP 2021

Xiaoyang Zhang, Junmin Xiao, Guangming Tan
ApproxTuner: a compiler and runtime system for adaptive approximations PPoPP 2021

Hashim Sharif, Yifan Zhao, Maria Kotsifakou, Akash Kothari, Ben Schreiber, Elizabeth Wang, Yasmin Sarita, Nathan Zhao, Keyur Joshi, Vikram S. Adve, Sasa Misailovic, Sarita V. Adve
TurboTransformers: an efficient GPU serving system for transformer models PPoPP 2021

Jiarui Fang, Yang Yu, Chengduo Zhao, Jie Zhou

Code: https://github.com/Tencent/TurboTransformers
DAPPLE: a pipelined data parallel approach for training large models PPoPP 2021

Shiqing Fan, Yi Rong, Chen Meng, Zongyan Cao, Siyu Wang, Zhen Zheng, Chuan Wu, Guoping Long, Jun Yang, Lixue Xia, Lansong Diao, Xiaoyong Liu, Wei Lin

Code: https://github.com/AlibabaPAI/DAPPLE
Corder: cache-aware reordering for optimizing graph analytics PPoPP 2021

YuAng Chen, Yeh-Ching Chung
DFOGraph: an I/O- and communication-efficient system for distributed fully-out-of-core graph processing PPoPP 2021

Jiping Yu, Wei Qin, Xiaowei Zhu, Zhenbo Sun, Jianqiang Huang, Xiaohan Li, Wenguang Chen
An efficient uncertain graph processing framework for heterogeneous architectures PPoPP 2021

Heng Zhang, Lingda Li, Donglin Zhuang, Rui Liu, Shuang Song, Dingwen Tao, Yanjun Wu, Shuaiwen Leon Song
Dynamic scaling for low-precision learning PPoPP 2021

Ruobing Han, Min Si, James Demmel, Yang You

Code: https://github.com/drcut/CPD
Exploring deep reuse in winograd CNN inference PPoPP 2021

Ruofan Wu, Feng Zhang, Zhen Zheng, Xiaoyong Du, Xipeng Shen
A novel memory-efficient deep learning training framework via error-bounded lossy compression PPoPP 2021

Sian Jin, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

HPCA

2017

Towards Pervasive and User Satisfactory CNN across GPU Microarchitectures HPCA 2017

Mingcong Song, Yang Hu, Huixiang Chen, Tao Li
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning HPCA 2017

Linghao Song, Xuehai Qian, Hai Li, Yiran Chen
FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks HPCA 2017

Wenyan Lu, Guihai Yan, Jiajun Li, Shijun Gong, Yinhe Han, Xiaowei Li

2018

Towards Efficient Microarchitectural Design for Accelerating Unsupervised GAN-based Deep Learning HPCA 2018

Mingcong Song, Jiaqi Zhang, Huixiang Chen, Tao Li
Making Memristive Neural Network Accelerators Reliable HPCA 2018

Ben Feinberg, Shibo Wang, Engin Ipek
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks HPCA 2018

Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Youngeun Kwon, Stephen W. Keckler
In-situ AI: Towards Autonomous and Incremental Deep Learning for IoT Systems HPCA 2018

Mingcong Song, Kan Zhong, Jiaqi Zhang, Yang Hu, Duo Liu, Weigong Zhang, Jing Wang, Tao Li

2019

HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array HPCA 2019

Linghao Song, Jiachen Mao, Youwei Zhuo, Xuehai Qian, Hai Li, Yiran Chen
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs HPCA 2019

Zhe Li, Caiwen Ding, Siyue Wang, Wujie Wen, Youwei Zhuo, Chang Liu, Qinru Qiu, Wenyao Xu, Xue Lin, Xuehai Qian, Yanzhi Wang

Code: https://github.com/lz1313/BlockCIrculantRNN
Bit Prudent In-Cache Acceleration of Deep Convolutional Neural Networks HPCA 2019

Xiaowei Wang, Jiecao Yu, Charles Augustine†, Ravi Iyer†, Reetuparna Das
Shortcut Mining: Exploiting Cross-Layer Shortcut Reuse in DCNN Accelerators HPCA 2019

Arash Azizimazreah, Lizhong Chen

2020

AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerator Arrays HPCA 2020

Linghao Song, Fan Chen, Youwei Zhuo, Xuehai Qian, Hai Li, Yiran Chen

2021

Heterogeneous Dataflow Accelerators for Multi-DNN Workloads HPCA 2021

Hyoukjun Kwon, Liangzhen Lai, Michael Pellauer, Tushar Krishna, Yu-Hsin Chen, Vikas Chandra
SPAGHETTI: Streaming Accelerators for Highly Sparse GEMM on FPGAs HPCA 2021

Reza Hojabr, Ali Sedaghati, Amirali Sharifian, Ahmad Khonsari, Arrvindh Shriraman

Code: https://github.com/sfu-arch/SPAGHETTI
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning HPCA 2021

Jiajun Li, Ahmed Louri, Avinash Karanth, Razvan Bunescu
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework HPCA 2021

Sung-En Chang, Yanyu Li, Mengshu Sun, Runbin Shi, Hayden K.-H. So, Xuehai Qian, Yanzhi Wang, Xue Lin
Revisiting HyperDimensional Learning for FPGA and Low-Power Architectures HPCA 2021

Mohsen Imani, Zhuowen Zou, Samuel Bosch, Sanjay Anantha Rao, Sahand Salamat, Venkatesh Kumar, Yeseong Kim, Tajana Rosing
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training HPCA 2021

Youngeun Kwon, Yunjae Lee, Minsoo Rhu
GradPIM: A Practical Processing-in-DRAM Architecture for Gradient Descent HPCA 2021

Heesu Kim, Hanmin Park, Taehyun Kim, Kwanheum Cho, Eojin Lee, Soojung Ryu, Hyuk-Jae Lee, Kiyoung Choi, Jinho Lee
SpaceA: Sparse Matrix Vector Multiplication on Processing-in-Memory Accelerator HPCA 2021

Xinfeng Xie, Zheng Liang, Peng Gu, Abanti Basak, Lei Deng, Ling Liang, Xing Hu, Yuan Xie
Layerweaver: Maximizing Resource Utilization of Neural Processing Units via Layer-Wise Scheduling HPCA 2021

Young H. Oh, Seonghak Kim, Yunho Jin, Sam Son, Jonghyun Bae, Jongsung Lee, Yeonhong Park, Dong Uk Kim, Tae Jun Ham, Jae W. Lee
Sentinel: Efficient Tensor Migration and Allocation on Heterogeneous Memory Systems for Deep Learning HPCA 2021

Jie Ren, Jiaolin Luo, Kai Wu, Minjia Zhang, Hyeran Jeon, Dong Li
CSCNN: Algorithm-hardware Co-design for CNN Accelerators using Centrosymmetric Filters HPCA 2021

Jiajun Li, Ahmed Louri, Avinash Karanth, Razvan Bunescu

MICRO

2017

Scale-Out Acceleration for Machine Learning MICRO 2017

Jongse Park, Hardik Sharma, Divya Mahajan, Joon Kyung Kim, Preston Olds, Hadi Esmaeilzadeh
Bit-Pragmatic Deep Neural Network Computing MICRO 2017

Jorge Albericio, Alberto Delmás, Patrick Judd, Sayeh Sharify, Gerard O'Leary, Roman Genov, Andreas Moshovos
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-Circulant Weight Matrices MICRO 2017

Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Xiaolong Ma, Yipeng Zhang, Jian Tang, Qinru Qiu, Xue Lin, Bo Yuan
DeftNN: Addressing Bottlenecks for DNN Execution on GPUs via Synapse Vector Elimination and Near-compute Data Fission MICRO 2017

Parker Hill, Animesh Jain, Mason Hill, Babak Zamirai, Chang-Hong Hsu, Michael A. Laurenzano, Scott Mahlke, Lingjia Tang, Jason Mars

2018

Addressing Irregularity in Sparse Neural Networks:A Cooperative Software/Hardware Approach MICRO 2018

Xi Zeng, Tian Zhi, Xuda Zhou, Zidong Du, Qi Guo, Shaoli Liu, Bingrui Wang, Yuanbo Wen, Chao Wang, Xuehai Zhou, Ling Li, Tianshi Chen, Ninghui Sun, Yunji Chen
Diffy: a Deja vu-Free Differential Deep Neural Network Accelerator MICRO 2018

Mostafa Mahmoud, Kevin Siu, Andreas Moshovos
Towards Memory Friendly Long-Short Term Memory Networks (LSTMs) on Mobile GPUs MICRO 2018

Xingyao Zhang, Chenhao Xie, Jing Wang, Weidong Zhang, Xin Fu
A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks MICRO 2018

Youjie Li, Jongse Park, Mohammad Alian, Yifan Yuan, Zheng Qu, Peitian Pan, Ren Wang, Alexander Gerhard Schwing, Hadi Esmaeilzadeh, Nam Sung Kim
PermDNN: Efficient Compressed Deep Neural Network Architecture with Permuted Diagonal Matrices MICRO 2018

Chunhua Deng, Siyu Liao, Yi Xie, Keshab K. Parhi, Xuehai Qian, Bo Yuan
Processing-in-Memory for Energy-efficient Neural Network Training: A Heterogeneous Approach MICRO 2018

Jiawen Liu, Hengyu Zhao, Matheus Almeida Ogleari, Dong Li, Jishen Zhao

2019

Wire-Aware Architecture and Dataflow for CNN Accelerators MICRO 2019

Sumanth Gudaparthi, Surya Narayanan, Rajeev Balasubramonian, Edouard Giacomin, Hari Kambalasubramanyam, Pierre-Emmanuel Gaillardon
Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture MICRO 2019

Yakun Sophia Shao, Jason Clemons, Rangharajan Venkatesan, Brian Zimmer, Matthew Fojtik, Nan Jiang, Ben Keller, Alicia Klinefelter, Nathaniel Pinckney, Priyanka Raina, Stephen G. Tell, Yanqing Zhang, William J. Dally, Joel Emer, C. Thomas Gray, Brucek Khailany, Stephen W. Keckler
ShapeShifter: Enabling Fine-Grain Data Width Adaptation in Deep Learning MICRO 2019

Alberto DelmásLascorz, Sayeh Sharify, Isak Edo, Dylan Malone Stuart, Omar Mohamed Awad, Patrick Judd, Mostafa Mahmoud, Milos Nikolic, Kevin Siu, Zissis Poulos, Andreas Moshovos
ZCOMP: Reducing DNN Cross-Layer Memory Footprint Using Vector Extensions MICRO 2019

Berkin Akin, Zeshan A. Chishti, Alaa R. Alameldeen
Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating MICRO 2019

Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, G. Edward Suh
SparTen: A Sparse Tensor Accelerator for Convolutional Neural Networks MICRO 2019

Ashish Gondimalla, Noah Chesnut, Mithuna Thottethodi, T. N. Vijaykumar
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM MICRO 2019

Skanda Koppula, Lois Orosa, A. Giray Yağlıkçı, Roknoddin Azizi, Taha Shahroodi, Konstantinos Kanellopoulos, Onur Mutlu
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference MICRO 2019

Chao-Tsung Huang, Yu-Chun Ding, Huan-Ching Wang, Chi-Wen Weng, Kai-Ping Lin, Li-Wei Wang, Li-De Chen
Efficient SpMV Operation for Large and Highly Sparse Matrices using Scalable Multi-way Merge Parallelization MICRO 2019

Fazle Sadi, Joe Sweeney, Tze Meng Low, James C. Hoe, Larry Pileggi, Franz Franchetti

2020

SuperNPU: An Extremely Fast Neural Processing Unit Using Superconducting Logic Devices MICRO 2020

Koki Ishida, Ilkwon Byun, Ikki Nagaoka, Kosuke Fukumitsu, Masamitsu Tanaka, Satoshi Kawakami, Teruo Tanimoto, Takatsugu Ono, Jangwoo Kim, and Koji Inoue
Printed Machine Learning Classifiers MICRO 2020

Muhammad Husnain Mubarik ,Dennis D. Weller, Nathaniel Bleier, Matthew Tomei Jasmin Aghassi-Hagmann, Mehdi B. Tahoori and Rakesh Kumar

Code: https://github.com/PrintedComputing
Look-Up Table based Energy Efficient Processing in Cache Support for Neural Network Acceleration MICRO 2020

Akshay Krishna Ramanathan, Gurpreet S Kalsi, Srivatsa Srinivasa, Tarun Makesh Chandran, Kamlesh R Pillai, Om J Omer, Vijaykrishnan Narayanan, Sreenivas Subramoney
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning MICRO 2020

Sheng-Chun Kao, Geonhwa Jeong, Tushar Krishna
VR-DANN: Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration MICRO 2020

Zhuoran Song, Feiyang Wu, Xueyuan Liu, Jing Ke, Naifeng Jing, Xiaoyao Liang
Procrustes: A Dataflow and Accelerator for Sparse Deep Neural Network Training MICRO 2020

Dingqing Yang, Amin Ghasemazar, Xiaowei Ren, Maximilian Golub, Guy Lemieux, Mieszko Lis
Duplo: Lifting Redundant Memory Accesses of Deep Neural Networks for GPU Tensor Cores MICRO 2020

Hyeonjin Kim , Sungwoo Ahn , Yunho Oh† , Bogil Kim , Won Woo Ro , William J. Song
DUET: Boosting Deep Neural Network Efficiency on Dual-Module Architecture MICRO 2020

Liu Liu, Zheng Qu, Lei Deng, Fengbin Tu, Shuangchen Li, Xing Hu, Zhenyu Gu, Yufei Ding, Yuan Xie
TFE: Energy-Efficient Transferred Filter-Based Engine to Compress and Accelerate Convolutional Neural Networks MICRO 2020

Huiyu Mo, Leibo Liu, Wenjing Hu, Wenping Zhu, Qiang Li, Ang Li, Shouyi Yin, Jian Chen, Xiaowei Jiang, Shaojun Wei
MatRaptor: A Sparse-Sparse Matrix Multiplication Accelerator Based on Row-Wise Product MICRO 2020

Nitish Srivastava, Hanchen Jin, Jie Liu, David Albonesi, Zhiru Zhang
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training MICRO 2020

Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh, Omar Mohamed Awad, Gennady Pekhimenko, Jorge Albericio, Andreas Moshovos
SAVE: Sparsity-Aware Vector Engine for Accelerating DNN Training and Inference on CPUs MICRO 2020

Zhangxiaowen Gong, Houxiang Ji, Christopher W. Fletcher, Christopher J. Hughes, Sara Baghsorkhi, Josep Torrellas
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference MICRO 2020

Ali Hadi Zadeh, Isak Edo, Omar Mohamed Awad, Andreas Moshovos

2021

EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference MICRO 2021

Thierry Tambe, Coleman Hooper, Lillian Pentecost, Tianyu Jia, En-Yu Yang, Marco Donato, Victor Sanh, Paul Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei

Code: https://github.com/harvard-acc/EdgeBERT
FPRaker: A Processing Element for Accelerating Neural Network Training MICRO 2021

Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh, Ciaran Bannon, Anand Jayarajan, Gennady Pekhimenko, Andreas Moshovos
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance MICRO 2021

Udit Gupta, Samuel Hsia, Jeff Zhang, Mark Wilkening, Javin Pombra, Hsien-Hsin Sean Lee, Gu-Yeon Wei, Carole-Jean Wu, David Brooks

Code: https://github.com/harvard-acc/RecPipe
Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving MICRO 2021

Qiyu Wan, Haojun Xia, Xingyao Zhang, Lening Wang, Shuaiwen Leon Song, Xin Fu

SC

2016

SERF: Efficient Scheduling for Fast Deep Neural Network Serving via Judicious Parallelism SC 2016

Feng Yan, Olatunji Ruwase, Yuxiong He, Evgenia Smirni
Optimizing Memory Efficiency for Deep Convolutional Neural Networks on GPUs SC 2016

Chao Li, Yi Yang, Min Feng, Srimat Chakradhar, Huiyang Zhou
dCUDA: Hardware Supported Overlap of Computation and Communication SC 2016

Tobias Gysi, Jeremia Bär, Torsten Hoefler
GreenLA: Green Linear Algebra Software for GPU-Accelerated Heterogeneous Computing SC 2016

Jieyang Chen, Li Tan, Panruo Wu, Dingwen Tao, Hongbo Li, Xin Liang, Sihuan Li, Rong Ge, Laxmi Bhuyan, Zizhong Chen
Merge-Based Parallel Sparse Matrix-Vector Multiplication (SpMV) SC 2016

Duane Merrill, Michael Garland

Code: https://github.com/dumerrill/mergespmv

2018

Large-Scale Hierarchical K-Means for Heterogeneous Many-Core Supercomputers SC 2018

Liandeng Li, Teng Yu, Wenlai Zhao, Haohuan Fu, Chenyu Wang, Li Tan, Guangwen Yang, John Thomson
TriCore: Parallel Triangle Counting on GPUs SC 2018

Yang Hu, Hang Liu, H. Howie Huang
PruneJuice: Pruning Trillion-Edge Graphs to a Precise Pattern-Matching Solution SC 2018

Tahsin Reza, Matei Ripeanu, Nicolas Tripoul, Geoffrey Sanders, Roger Pearce

Code: https://github.com/LLNL/havoqgt
Exploring Flexible Communications for Streamlining DNN Ensemble Training Pipelines SC 2018

Randall Pittman, Hui Guan, Xipeng Shen, Seung-Hwan Lim, Robert M. Patton
Anatomy of High-Performance Deep Learning Convolutions on SIMD Architectures SC 2018

Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke
Fault Tolerant One-Sided Matrix Decompositions on Heterogeneous Systems with GPUs SC 2018

Jieyang Chen, Hongbo Li, Sihuan Li, Xin Liang, Panruo Wu, Dingwen Tao, Kaiming Ouyang, Yuanlai Liu, Kai Zhao, Qiang Guan, Zizhong Chen

2019

Large-Batch Training for LSTM and Beyond SC 2019

Yang You, Jonathan Hseu, Chris Ying, James Demmel, Kurt Keutzer, Cho-Jui Hsieh
Channel and Filter Parallelism for Large-Scale CNN Training SC 2019

Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen
SparCML: High-Performance Sparse Communication for Machine Learning SC 2019

Cedric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan Alistarh, and Torsten Hoefler
PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration SC 2019

Sangkug Lym, Esha Choukse, Siavash Zangeneh, Wei Wen, Sujay Sanghavi, Mattan Erez
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research SC 2019

Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens
BSTC: A Novel Binarized-Soft-Tensor-Core Design for Accelerating Bit-Based Approximated Neural Nets SC 2019

Ang Li, Tong Geng, Tianqi Wang, Martin Herbordt, Shuaiwen Leon Song, Kevin Barker

Code: https://github.com/uuudown/SBNN

2020

A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery SC 2020

Ankit Srivastava, Sriram Chockalingam, Srinivas Aluru

Code: https://github.com/asrivast28/ramBLe
Recurrent Neural Network Architecture Search for Geophysical Emulation SC 2020

Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash

Code: https://github.com/rmjcs2020/Geophysical_NAS
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity SC 2020

Cong Guo, Bo Yang Hsueh, Jingwen Leng, Yuxian Qiu, Yue Guan, Zehuan Wang, Xiaoying Jia, Xipeng Li, Minyi Guo, Yuhao Zhu

Code: https://github.com/clevercool/TileSparsity
Sparse GPU Kernels for Deep Learning SC 2020

Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen

Code: https://github.com/google-research/sputnik
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA SC 2020

Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen, Aleksandr Drozd, Jens Domke, Lingqi Zhang, Ryousei Takano, Satoshi Matsuoka

Code: https://github.com/wahibium/SC20-KARMA-AD-Appendix-Description-
ZeRO: Memory optimizations Toward Training Trillion Parameter Models SC 2020

Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He
Kraken: Memory-Efficient Continual Learning for Large-Scale Real-Time Recommendations SC 2020

Minhui Xie, Kai Ren, Youyou Lu, Guangxu Yang,Qingxing Xu, Bihai Wu, Jiazhen Lin, Hongbo Ao, Wanhong Xu, Jiwu Shu

Code: https://github.com/adamadagradsgd/Kraken
Optimizing Deep Learning Recommender Systems Training on CPU Cluster Architectures SC 2020

Dhiraj Kalamkar, Evangelos Georganas, Sudarshan Srinivasan, Jianping Chen, Mikhail Shiryaev, Alexander Heinecke
Herring: Rethinking the Parameter Server at Scale for the Cloud SC 2020

Indu Thangakrishnan, Derya Cavdar, Can Karakus, Piyush Ghai, Yauheni Selivonchyk, Cory Pruce
GEMS: GPU-Enabled Memory-Aware Model-Parallelism System for Distributed DNN Training SC 2020

Arpan Jain, Ammar Ahmad Awan, Asmaa M. Aljuhani, Jahanzeb Maqbool Hashmi, Quentin G. Anthony, Hari Subramoni, Dhabaleswar K. Panda, Raghu Machiraju, Anil Parwani
Newton-ADMM: A Distributed GPU-Accelerated Optimizer for Multiclass Classification Problems SC 2020

Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama

Code: https://github.com/fang150/Newton_ADMM
Reducing Communication in Graph Neural Network Training SC 2020

Alok Tripathy, Katherine Yelick, Aydın Buluç

Code: https://github.com/PASSIONLab/CAGNET
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems SC 2020

Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang

Code: https://github.com/dglai/FeatGraph
GE-SpMM: General-Purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Guyue Huang, Guohao Dai, Yu Wang, Huazhong Yang

Code: https://github.com/hgyhungry/ge-spmm

2021

KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks SC 2021

J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang

Code: https://github.com/gpauloski/kfac_pytorch
Tensor Processing Primitives: A Programming Abstraction for Efficiency And Portability in Deep Learning Workloads SC 2021

Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke
ET: Re-Thinking Self-Attention for Transformer Models on GPUs SC 2021

Weihao Cui, Han Zhao, Quan Chen, Ningxin Zheng, Jingwen Leng, Jieru Zhao, Zhuo Song, Tao Ma, Yong Yang, Chao Li, Minyi Guo

Code: https://github.com/cctry/SCpaper-2021/tree/aedab163f44bff8dfad3745d4f57972cb7640cda
Parallel Construction of Module Networks SC 2021

Ankit Srivastava, Sriram P. Chockalingam, Maneesha Aluru, Srinivas Aluru

Code: https://github.com/asrivast28/ParsiMoNe
Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines SC 2021

Shigang Li, Torsten Hoefler

Code: https://github.com/Shigangli/Chimera
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores SC 2021

Boyuan Feng, Yuke Wang, Tong Geng, Ang Li, Yufei Ding

Code: https://github.com/BoyuanFeng/APNN-TC
Scalable Edge-based Hyperdimensional Learning System with Brain-like Neural Adaptation SC 2021

Zhuowen Zou, Yeseong Kim, Farhad Imani, Haleh Alimohamadi, Rosario Cammarota, Mohsen Imani
Dr. Top-k: Delegate-Centric Top-k Computation on GPUs SC 2021

Anil Gaihre, Da Zheng, Scott Weitze, Lingda Li, Shuaiwen Leon Song, Caiwen Ding, Xiaoye S. Li, Hang Liu
Distributed Multigrid Neural Solver on Megavoxel Domains SC 2021

Aditya Balu, Sergio Botelho, Biswajit Khara, Vinay Rao, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Santi Adavani, Baskar Ganapathysubramanian
Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM SC 2021

Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia
ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning SC 2021

Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers SC 2021

Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks SC 2021

Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj Kalamkar, Nesreen K. Ahmed, Sasikanth Avancha

Code: dmlc/dgl#2914 (commit: cfb73e2)
Efficient Scaling of Dynamic Graph Neural Networks SC 2021

Venkatesan T. Chakaravarthy, Shivmaran S. Pandian, Saurabh Raje, Yogish Sabharwal, Toyotaro Suzumura, Shashanka Ubaru
Efficient Tensor Core-Based GPU Kernels for Structured Sparsity Under Reduced Precision SC 2021

Zhaodong Chen, Zheng Qu, Liu Liu, Yufei Ding, Yuan Xie

Code: https://github.com/apuaaChen/vectorSparse

NSDI

2017

Clipper: A Low-Latency Online Prediction Serving System NSDI 2017

Daniel Crankshaw, Xin Wang, Guilio Zhou, Michael J. Franklin, Joseph E. Gonzalez, Ion Stoica
Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds NSDI 2017

Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, and Phillip B. Gibbons, Onur Mutlu
TUX2: Distributed Graph Computation for Machine Learning NSDI 2017

Wencong Xiao, Jilong Xue, Youshan Miao, Zhen Li, Cheng Chen, Ming Wu, Wei Li, Lidong Zhou

2019

Janus: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs NSDI 2019

Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dong-Jin Shin, and Byung-Gon Chun
BLAS-on-flash: An Efficient Alternative for Large Scale ML Training and Inference? NSDI 2019

Suhas Jayaram Subramanya and Harsha Vardhan Simhadri, Srajan Garg, Anil Kag and Venkatesh Balasubramanian

Code: https://github.com/Microsoft/BLAS-on-flash
Tiresias: A GPU Cluster Manager for Distributed Deep Learning NSDI 2019

Juncheng Gu, Mosharaf Chowdhury, and Kang G. Shin, Yibo Zhu, Myeongjae Jeon, Junjie Qian, Hongqiang Liu, Chuanxiong Guo

Code: https://github.com/SymbioticLab/Tiresias

2021

Elastic Resource Sharing for Distributed Deep Learning NSDI 2021

Changho Hwang and Taehyun Kim, Sunghyun Kim, Jinwoo Shin and KyoungSoo Park
ATP: In-network Aggregation for Multi-tenant Learning NSDI 2021

ChonLam Lao, Yanfang Le and Kshiteej Mahajan, Yixi Chen and Wenfei Wu, Aditya Akella, Michael Swift
On the Use of ML for Blackbox System Performance Prediction NSDI 2021

Silvery Fu, Saurabh Gupta and Radhika Mittal, Sylvia Ratnasamy
Scaling Distributed Machine Learning with In-Network Aggregation NSDI 2021

Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan Ports, Peter Richtarik

ISCA

2017

In-Datacenter Performance Analysis of a Tensor Processing Unit ISCA 2017

Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, C. Richard Ho, Doug Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Daniel Killebrew, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Matt Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter, Walter Wang, Eric Wilcox, Doe Hyun Yoon
SCALEDEEP: A Scalable Compute Architecture for Learning and Evaluating Deep Networks ISCA 2017

Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, Anand Raghunathan
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks ISCA 2017

Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Stephen W. Keckler, William J. Dally
Maximizing CNN Accelerator Efficiency Through Resource Partitioning ISCA 2017

Yongming Shen, Michael Ferdman, Peter Milder
Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism ISCA 2017

Jiecao Yu, Andrew Lukefahr, David Palframan, Ganesh Dasika, Reetuparna Das, Scott Mahlke
Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent ISCA 2017

Christopher De Sa, Matthew Feldman, Christopher Ré, Kunle Olukotun

2018

PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms ISCA 2018

Prakalp Srivastava, Mingu Kang, Sujan K. Gonugondla, Sungmin Lim, Jungwook Choi, Vikram Adve, Nam Sung Kim, Naresh Shanbhag
Computation Reuse in DNNs by Exploiting Input Similarity ISCA 2018

Marc Riera, Jose-Maria Arnau, Antonio González
GenAx: A Genome Sequencing Accelerator ISCA 2018

Daichi Fujiki, Aran Subramaniyan, Tianjun Zhang, Yu Zeng, Reetuparna Das, David Blaauw, Satish Narayanasamy
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks ISCA 2018

Amir Yazdanbakhsh, Kambiz Samadi, Nam Sung Kim, Hadi Esmaeilzadeh
SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks ISCA 2018

Vahideh Akhlaghi, Amir Yazdanbakhsh, Kambiz Samadi, Rajesh K. Gupta, Hadi Esmaeilzadeh
UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition ISCA 2018

Kartik Hegde, Jiyong Yu, Rohit Agrawal, Mengjia Yan, Michael Pellauer, Christopher W. Fletcher
Energy-Efficient Neural Network Accelerator Based on Outlier-Aware Low-Precision Computation ISCA 2018

Eunhyeok Park, Dongyoung Kim, Sungjoo Yoo
Prediction based Execution on Deep Neural Networks ISCA 2018

Mingcong Song, Jiechen Zhao, Yang Hu, Jiaqi Zhang, Tao Li
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network ISCA 2018

Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Vikas Chandra, Hadi Esmaeilzadeh
Gist: Efficient Data Encoding for Deep Neural Network Training ISCA 2018

Animesh Jain, Amar Phanishayee, Jason Mars, Lingjia Tang, Gennady Pekhimenko
The Dark Side of DNN Pruning ISCA 2018

Reza Yazdani, Marc Riera, Jose-Maria Arnau, Antonio González

2019

Sparse ReRAM Engine: Joint exploration of activation and weight sparsity on compressed neural network ISCA 2019

Tzu-Hsien Yang, Hsiang-Yun Cheng, Chia-Lin Yang, I-Ching Tseng, Han-Wen Hu, Hung-Sheng Chang, Hsiang-Pang Li
MnnFast: A Fast and Scalable System Architecture for Memory-Augmented Neural Networks ISCA 2019

Hanhwi Jang, Joonsung Kim, Jae-Eon Jo, Jaewon Lee, Jangwoo Kim
TIE: Energy-efficient tensor train-based inference engine for deep neural network ISCA 2019

Chunhua Deng, Fangxuan Sun, Xuehai Qian, Jun Lin, Zhongfeng Wang, Bo Yuan
Accelerating Distributed Reinforcement Learning with In-Switch Computing ISCA 2019

Youjie Li, Iou-Jen Liu, Yifan Yuan, Deming Chen, Alexander Schwing, Jian Huang
Eager Pruning: Algorithm and Architecture Support for Fast Training of Deep Neural Networks ISCA 2019

Jiaqi Zhang, Xiangru Chen, Mingcong Song, Tao Li
Laconic Deep Learning Inference Acceleration ISCA 2019

Sayeh Sharify, Alberto Delmas Lascorz, Mostafa Mahmoud, Milos Nikolic, Kevin Siu, Dylan Malone Stuart, Zissis Poulos, Andreas Moshovos
DeepAttest: An End-to-End Attestation Framework for Deep Neural Networks ISCA 2019

Huili Chen, Cheng Fu, Bita Darvish Rouhani, Jishen Zhao, Farinaz Koushanfar

2020

High-Performance Deep-Learning Coprocessor Integrated into x86 SoC with Server-Class CPUs ISCA 2020

Glenn Henry, Parviz Palangpour, Michael Thomson, J Scott Gardner, Bryce Arden, Kimble Houck, Jonathan Johnson, Kyle O'Brien, Scott Petersen, Benjamin Seroussi, Tyler Walker
Think Fast: A Tensor Streaming Processor (TSP) for Accelerating Deep Learning Workloads ISCA 2020

Dennis Abts, Jonathan Ross, Jon Sparling, Mark Wong-VanHaren, Max Baker, Tom Hawkins, Andrew Bell, John Thompson, Teme Kahsai, Garrin Kimmell, Jennifer Hwang, Rebekah Leslie-Hurd, Michael Bye, Rogan Creswick, Matthew Boyd, Mahitha Venigalla, Evan Laforge, Jon Purdy, Utham Kamath, Dinesh Maheshwari, Michael Beidler, Geert Rosseel, Omar Ahmad, Gleb Gagarin, Rick Czekalski, Ashay Rane, Sahil Parmar
Gorgon: Accelerating Machine Learning from Relational Data ISCA 2020

Matthew Vilim, Alexander Rucker, Yaqi Zhang, Sophia Liu, Kunle Olukotun
SpinalFlow: An Architecture and Dataflow Tailored for Spiking Neural Networks ISCA 2020

Surya Narayanan, Karl Taht, Rajeev Balasubramonian, Edouard Giacomin, Pierre-Emmanuel Gaillardon
NEBULA: A Neuromorphic Spin-Based Ultra-Low Power Architecture for SNNs and ANNs ISCA 2020

Sonali Singh, Anup Sarma, Nicholas Jao, Ashutosh Pattnaik, Sen Lu, Kezhou Yang, Abhronil Sengupta, Vijaykrishnan Narayanan, Chita R. Das
uGEMM: Unary Computing Architecture for GEMM Applications ISCA 2020

Di Wu, Jingjie Li, Ruokai Yin, Hsuan Hsiao, Younghyun Kim, Joshua San Miguel
Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs ISCA 2020

Esha Choukse, Michael B. Sullivan, Mike O’Connor, Mattan Erez, Jeff Pool, David Nellans, Stephen W. Keckler
A Multi-Neural Network Acceleration Architecture ISCA 2020

Eunjin Baek, Dongup Kwon, Jangwoo Kim
SmartExchange: Trading Higher-Cost Memory Storage/Access for Lower-Cost Computation ISCA 2020

Yang Zhao, Xiaohan Chen, Yue Wang, Chaojian Li, Haoran You, Yonggan Fu, Yuan Xie, Zhangyang Wang, Yingyan Lin
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations ISCA 2020

Ranggi Hwang, Taehun Kim, Youngeun Kwon, Minsoo Rhu
DeepRecSys: A System for Optimizing End-to-End At-Scale Neural Recommendation Inference ISCA 2020

Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu
An In-Network Architecture for Accelerating Shared-Memory Multiprocessor Collectives ISCA 2020

Benjamin Klenk, Nan Jiang, Greg Thorson, Larry Dennison
DRQ: Dynamic Region-Based Quantization for Deep Neural Network Acceleration ISCA 2020

Zhuoran Song, Bangqi Fu, Feiyang Wu, Zhaoming Jiang, Li Jiang, Naifeng Jing, Xiaoyao Liang

2021

RaPiD: AI Accelerator for Ultra-Low Precision Training and Inference ISCA 2021

Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Wang, Sanchari Sen, Jintao Zhang, Ankur Agrawal, Monodeep Kar, Shubham Jain, Alberto Mannari, Hoang Tran, Yulong Li, Eri Ogawa, Kazuaki Ishizaki, Hiroshi Inoue, Marcel Schaal, Mauricio Serrano, Jungwook Choi, Xiao Sun, Naigang Wang, Chia-Yu Chen, Allison Allain, James Bonano, Nianzheng Cao, Robert Casatuta, Matthew Cohen, Bruce Fleischer, Michael Guillorn, Howard Haynie, Jinwook Jung, Mingu Kang, Kyu-hyoun Kim, Siyu Koswatta, Saekyu Lee, Martin Lutz, Silvia Mueller, Jinwook Oh, Ashish Ranjan, Zhibin Ren, Scot Rider, Kerstin Schelm, Michael Scheuermann, Joel Silberman, Jie Yang, Vidhi Zalani, Xin Zhang, Ching Zhou, Matt Ziegler, Vinay Shah, Moriyoshi Ohara, Pong-Fei Lu, Brian Curran, Sunil Shukla, Leland Chang, Kailash Gopalakrishnan
REDUCT: Keep It Close, Keep It Cool! - Scaling DNN Inference on Multi-Core CPUs with Near-Cache Compute ISCA 2021

Anant V. Nori, Rahul Bera, Shankar Balachandran, Joydeep Rakshit, Om J. Omer, Avishaii Abuhatzera, Belliappa Kuttanna, Sreenivas Subramoney
Communication Algorithm-Architecture Co-Design for Distributed Deep Learning ISCA 2021

Jiayi Huang, Pritam Majumder, Sungkeun Kim, Abdullah Muzahid, Ki Hwan Yum, Eun Jung Kim
Hetero-ViTAL: A Virtualization Stack for Heterogeneous FPGA Clusters ISCA 2021

Yue Zha, Jing LiYue Zha, Jing Li
Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms ISCA 2021

Saeed Rashidi, Matthew Denton, Srinivas Sridharan, Sudarshan Srinivasan, Amoghavarsha Suresh, Jade Nie, Tushar Krishna
CoSA: Scheduling by Constrained Optimization for Spatial Accelerators ISCA 2021

Qijing Huang, Aravind Kalaiah, Minwoo Kang, James Demmel, Grace Dinh, John Wawrzynek, Thomas Norell, Yakun Sophia Shao
η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities ISCA 2021

Xingyao Zhang, Haojun Xia, Donglin Zhuang, Hao Sun, Xin Fu, Michael B. Taylor, Shuaiwen Leon Song
SPACE: Locality-Aware Processing in Heterogeneous Memory for Personalized Recommendations ISCA 2021

Hongju Kal, Seokmin Lee, Gun Ko, Won Woo Ro
ELSA: Hardware-Software Co-Design for Efficient, Lightweight Self-Attention Mechanism in Neural Networks ISCA 2021

Tae Jun Ham, Yejin Lee, Seong Hoon Seo, Soosung Kim, Hyunji Choi, Sung Jun Jung, Jae W. Lee
Cambricon-Q: A Hybrid Architecture for Efficient Training ISCA 2021

Yongwei Zhao, Chang Liu, Zidong Du, Qi Guo, Xing Hu, Yimin Zhuang, Zhenxing Zhang, Xinkai Song, Wei Li, Xishan Zhang, Ling Li, Zhiwei Xu, Tianshi Chen
TENET: A Framework for Modeling Tensor Dataflow Based on Relation-Centric Notation ISCA 2021

Liqiang Lu, Naiqing Guan, Yuyue Wang, Liancheng Jia, Zizhang Luo, Jieming Yin, Jason Cong, Yun Liang

Code: https://github.com/pku-liang/TENET
NASGuard: A Novel Accelerator Architecture for Robust Neural Architecture Search (NAS) Networks ISCA 2021

Xingbin Wang, Boyan Zhao, Rui Hou, Amro Awad, Zhihong Tian, Dan Meng
NASA: Accelerating Neural Network Design with a NAS Processor ISCA 2021

Xiaohan Ma, Chang Si, Ying Wang, Cheng Liu, Lei Zhang
NN-Baton: DNN Workload Orchestration and Chiplet Granularity Exploration for Multichip Accelerators ISCA 2021

Zhanhong Tan, Hongyu Cai, Runpei Dong, Kaisheng Ma
HASCO: Towards Agile Hardware and Software CO-design for Tensor Computation ISCA 2021

Qingcheng Xiao, Size Zheng, Bingzhe Wu, Pengcheng Xu, Xuehai Qian, Yun Liang

Code: https://github.com/pku-liang/HASCO
Dual-Side Sparse Tensor Core ISCA 2021

Yang Wang, Chen Zhang, Zhiqiang Xie, Cong Guo, Yunxin Liu, Jingwen Leng
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging ISCA 2021

Chao-Tsung Huang
GoSPA: An Energy-Efficient High-Performance Globally Optimized SParse Convolutional Neural Network Accelerator ISCA 2021

Chunhua Deng, Yang Sui, Siyu Liao, Xuehai Qian, Bo Yuan

SysML

2018 (Session only)

TVM: End-to-End Compilation Stack for Deep Learning SysML 2018

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Robust Gradient Descent via Moment Encoding with LDPC Codes SysML 2018

Raj Kumar Maity, Ankit Singh Rawat, Arya Mazumdar
Analog electronic deep networks for fast and efficient inference SysML 2018

Jonathan Binas, Daniel Neil, Giacomo Indiveri, Shih-Chii Liu, Michael Pfeiffer
YellowFin: Adaptive optimization for (A)synchronous systems SysML 2018

Jian Zhang, Ioannis Mitliagkas
Understanding the Limitations of Current Energy-Efficient Design Approaches for Deep Neural Networks SysML 2018

Yu-Hsin Chen, Tien-Ju Yang, Joel Emer, Vivienne Sze
"I Like the Way You Think!" - Inspecting the Internal Logic of Recurrent Neural Networks SysML 2018

Thibault Sellam, Kevin Lin, Ian Yiran Huang, Carl Vondrick, Eugene Wu

2019

TicTac: Accelerating Distributed Deep Learning with Communication Scheduling SysML 2019

Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy Campbell

Code: https://github.com/xldrx/tictac
Priority-based Parameter Propagation for Distributed DNN Training SysML 2019

Anand Jayarajan, Jinliang Wei, Garth Gibson, Alexandra Fedorova, Gennady Pekhimenko
BlueConnect: Decomposing All-Reduce for Deep Learning on Heterogeneous Network Hierarchy SysML 2019

Minsik Cho, Ulrich Finkler, David Kung
Beyond Data and Model Parallelism for Deep Neural Networks SysML 2019

Zhihao Jia, Matei Zaharia, Alex Aiken
ParMAC: Distributed Optimisation of Nested Functions, with Application to Learning Binary Autoencoders SysML 2019

Miguel A Carreira-Perpinan, Mehdi Alizadeh
3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning SysML 2019

Hyeontaek Lim, David G Andersen, Michael Kaminsky
Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD SysML 2019

Jianyu Wang, Gauri Joshi

Code: https://github.com/meliketoy/wide-resnet.pytorch
YellowFin and the Art of Momentum Tuning SysML 2019

Jian Zhang, Ioannis Mitliagkas
AGGREGATHOR: Byzantine Machine Learning via Robust Gradient Aggregation SysML 2019

Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Arsany Guirguis, Sébastien Rouault

Code: https://github.com/LPD-EPFL/AggregaThor
Towards Federated Learning at Scale: System Design SysML 2019

Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification SysML 2019

Qi Lei, Lingfei Wu, Pin-Yu Chen, Alex Dimakis, Inderjit Dhillon, Michael J Witbrock

Code: https://github.com/cecilialeiqi/adversarial_text
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression SysML 2019

Ilia Shumailov, Yiren Zhao, Robert Mullins, Ross Anderson
Scaling Video Analytics on Constrained Edge Nodes SysML 2019

Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G Andersen, Michael Kaminsky, Subramanya R. Dulloor
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video SysML 2019

Huizi Mao, Taeyoung Kong, William J. Dally
AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling SysML 2019

Ting-Wu Chin, Ruizhou Ding, Diana Marculescu
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning SysML 2019

Paul Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas Venkataramanaiah, Jae-sun Seo, Matthew Mattina
Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator SysML 2019

Tian Zhao, Yaqi Zhang, Kunle Olukotun
Restructuring Batch Normalization to Accelerate CNN Training SysML 2019

Wonkyung Jung, Daejin Jung, Byeongho Kim, Sunjung Lee, Wonjong Rhee, Jung Ho Ahn

Code: https://github.com/scale-snu/caffe-bn-restructuring, https://github.com/scale-snu/mkldnn-bn-restructuring
Bandana: Using Non-Volatile Memory for Storing Deep Learning Models SysML 2019

Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim Hazelwood, Asaf Cidon, Sachin Katti
Mini-batch Serialization: CNN Training with Inter-layer Data Reuse SysML 2019

Sangkug Lym, Armand Behroozi, Wei Wen, Ge Li, Yongkee Kwon, Mattan Erez
Accurate and Efficient 2-bit Quantized Neural Networks SysML 2019

Jungwook Choi, Swagath Venkataramani, Vijayalakshmi (Viji) Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, Pierce Chuang
Full Deep Neural Network Training on a Pruned Weight Budget SysML 2019

Mieszko Lis, Maximilian Golub, Guy Lemieux
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment SysML 2019

Cedric Renggli, Bojan Karlaš, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang
Data Validation for Machine Learning SysML 2019

Neoklis Polyzotis, Martin Zinkevich, Sudip Roy, Eric Breck, Steven Whang

Code: https://github.com/tensorflow/data-validation
Kernel machines that adapt to GPUs for effective large batch training SysML 2019

Siyuan Ma, Mikhail Belkin

Code: https://github.com/EigenPro/EigenPro2
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications SysML 2019

Dibakar Gope, Ganesh Dasika, Matthew Mattina
Optimizing DNN Computation with Relaxed Graph Substitutions SysML 2019

Zhihao Jia, James Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, Alex Aiken

Code: https://github.com/jiazhihao/metaflow_sysml19
Pytorch-BigGraph: A Large Scale Graph Embedding System SysML 2019

Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich

Code: https://github.com/facebookresearch/PyTorch-BigGraph
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning SysML 2019

Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki

Code: https://github.com/rlgraph/rlgraph
TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning SysML 2019

Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, Shanqing Cai
AutoGraph: Imperative-style Coding with Graph-based Performance SysML 2019

Dan Moldovan, James Decker, Fei Wang, Andrew Johnson, Brian Lee, Zack Nado, D Sculley, Tiark Rompf, Alexander B Wiltschko

Code: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/autograph
TensorFlow.js: Machine Learning for the Web and Beyond SysML 2019

Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Charles Nicholson, Nick Kreeger, Ping Yu, Shanqing Cai, Eric Nielsen, David Soegel, Stan Bileschi, Michael Terry, Ann Yuan, Kangyi Zhang, Sandeep Gupta, Sarah Sirajuddin, D Sculley, Rajat Monga, Greg Corrado, Fernanda Viegas, Martin M Wattenberg

2020

A System for Massively Parallel Hyperparameter Tuning SysML 2020

Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-tzur, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

Code: https://github.com/liamcli/darts_asha
PLink: Discovering and Exploiting Locality for Accelerated Distributed Training on the public Cloud SysML 2020

Liang Luo, Peter West, Jacob Nelson, Arvind Krishnamurthy, Luis Ceze
Federated Optimization in Heterogeneous Networks SysML 2020

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Code: https://github.com/litian96/FedProx
BPPSA: Scaling Back-propagation by Parallel Scan Algorithm SysML 2020

Shang Wang, Yifan Bai, Gennady Pekhimenko

Code: https://github.com/UofT-EcoSystem/BPPSA-open
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems SysML 2020

Weijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, Ping Li
Resource Elasticity in Distributed Deep Learning SysML 2020

Andrew Or, Haoyu Zhang, Michael Freedman
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems SysML 2020

Beidi Chen, Tharun Medini, James Farwell, sameh gobriel, Charlie Tai, Anshumali Shrivastava

Code: https://github.com/keroro824/HashingDeepLearning
FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks SysML 2020

Hui Guan, Laxmikant Kishor Mokadam, Xipeng Shen, Seung-Hwan Lim, Robert Patton
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization SysML 2020

Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Joseph Gonzalez, Kurt Keutzer, Ion Stoica

Code: https://github.com/parasj/checkmate
What is the State of Neural Network Pruning? SysML 2020

Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John Guttag

Code: https://github.com/jjgo/shrinkbench
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems SysML 2020

Xiaofan Zhang, Haoming Lu, Cong Hao, Jiachen Li, Bowen Cheng, Yuhong Li, Kyle Rupnow, Jinjun Xiong, Thomas Huang, Honghui Shi, Wen-Mei Hwu, Deming Chen
MNN: A Universal and Efficient Inference Engine SysML 2020

Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lyu, Zhihua Wu

Code: https://github.com/alibaba/MNN
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference SysML 2020

Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia

Code: https://github.com/stanford-futuredata/Willump
Attention-based Learning for Missing Data Imputation in HoloClean SysML 2020

Richard Wu, Aoqian Zhang, Ihab Ilyas, Theodoros Rekatsinas
Privacy-Preserving Bandits SysML 2020

Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Ben Livshits

Code: https://github.com/mmalekzadeh/privacy-preserving-bandits
Understanding the Downstream Instability of Word Embeddings SysML 2020

Megan Leszczynski, Avner May, Jian Zhang, Sen Wu, Christopher Aberger, Christopher Re

Code: https://github.com/HazyResearch/anchor-stability
Model Assertions for Monitoring and Improving ML Models SysML 2020

Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning SysML 2020

Ameer Haj-Ali, Qijing (Jenny) Huang, John Xiang, William Moses, Krste Asanovic, John Wawrzynek, Ion Stoica

Code: https://github.com/ucb-bar/autophase
Automatically batching control-intensive programs for modern accelerators SysML 2020

Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew Hoffman, Rif A. Saurous
Predictive Precompute with Recurrent Neural Networks SysML 2020

Hanson Wang, Zehui Wang, Yuanyuan Ma
Sense & Sensitivities: The Path to General-Purpose Algorithmic Differentiation SysML 2020

Mike Innes
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices SysML 2020

Byung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin, Hsin-Pai Cheng, Jilei Hou, Hadi Esmaeilzadeh
Fine-Grained GPU Sharing Primitives for Deep Learning Applications SysML 2020

Peifeng Yu, Mosharaf Chowdhury

Code: https://github.com/SymbioticLab/Salus
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc SysML 2020

Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, Alex Aiken
OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator SysML 2020

Junki Park, Hyunsung Yoon, Daehyun Ahn, Jungwook Choi, Jae-Joon Kim
PoET-BiN: Power Efficient Tiny Binary Neurons SysML 2020

Sivakumar Chidambaram, Pierre Langlois, Jean-Pierre David
Memory-Driven Mixed Low Precision Quantization for Enabling Deep Network Inference on Microcontrollers SysML 2020

Manuele Rusci, Alessandro Capotondi, Luca Benini

Code: https://github.com/mrusci/training-mixed-precision-quantized-networks
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks SysML 2020

Sambhav Jain, Albert Gural, Michael Wu, Chris Dick

Code: https://github.com/Xilinx/graffitist
Riptide: Fast End-to-End Binarized Neural Networks SysML 2020

Joshua Fromm, Meghan Cowan, Matthai Philipose, Luis Ceze, Shwetak Patel
Searching for Winograd-aware Quantized Networks SysML 2020

Javier Fernandez-Marques, Paul Whatmough, Andrew Mundy, Matthew Mattina
Blink: Fast and Generic Collectives for Distributed ML SysML 2020

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Nikhil Devanur, Jorgen Thelin, Ion Stoica
A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms SysML 2020

Yu Wang, Gu-Yeon Wei, David Brooks

Code: https://github.com/Emma926/paradnn
MotherNets: Rapid Deep Ensemble Learning SysML 2020

Abdul Wasay, Brian Hentschel, Yuze Liao, Sanyuan Chen, Stratos Idreos
MLPerf Training Benchmark SysML 2020

Peter Mattson, Christine Cheng, Gregory Diamos, Cody Coleman, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia

Code: https://github.com/mlcommons/training

2021

Boveda: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick SysML 2021

Isak Edo Vivancos, Sayeh Sharify, Daniel Ly-Ma, Ameer Abdelhadi, Ciaran Bannon, Milos Nikolic, Mostafa Mahmoud, Alberto
To Bridge Neural Network Design and Real-World Performance: A Behaviour Study for Neural Networks SysML 2021

Xiaohu Tang, Shihao Han, Li Lyna Zhang, Ting Cao, Yunxin Liu
Cortex: A Compiler for Recursive Deep Learning Models SysML 2021

Pratik Fegade, Tianqi Chen, Phillip Gibbons, Todd Mowry
Adaptive Gradient Communication via Critical Learning Regime Identification SysML 2021

Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris Papailiopoulos

Code: https://github.com/uw-mad-dash/Accordion
EXPLORING THE LIMITS OF CONCURRENCY IN ML TRAINING ON GOOGLE TPUS SysML 2021

Sameer Kumar, Yu Wang, Cliff Young, James Bradbury, Naveen Kumar, Dehao Chen, Andy Swing
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy SysML 2021

Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus

Code: https://github.com/lucaslie/torchprune
Learning Fitness Functions for Machine Programming SysML 2021

Shantanu Mandal, Todd Anderson, Javier Turek, Justin Gottschlich, Shengtian Zhou, Abdullah Muzahid
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More SysML 2021

Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao, Anshumali Shrivastava
IOS: Inter-Operator Scheduler for CNN Acceleration SysML 2021

Yaoyao Ding, Ligeng Zhu, Zhihao Jia, Gennady Pekhimenko, Song Han

Code: https://github.com/mit-han-lab/inter-operator-scheduler
A Deep Learning Based Cost Model for Automatic Code Optimization SysML 2021

Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham LEGHETTAS, Kamel Abdous, Taha Arbaoui, Karima BENATCHBA, Saman amarasinghe
Don't Forget to Sign the Gradients! SysML 2021

Omid Aramoon, Pin-Yu Chen, Gang Qu
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference SysML 2021

Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators SysML 2021

Hamzah Abdelaziz, ali shafiee, Jong Hoon Shin, Ardavan Pedram, Joseph Hassoun
Swift for TensorFlow: A portable, flexible platform for deep learning SysML 2021

Brennan Saeta, Denys Shabalin

Code: t https://github.com/tensorflow/swift-models
Equality Saturation for Tensor Graph Superoptimization SysML 2021

Yichen Yang, Phitchaya Phothilimthana, Yisu Wang, Max Willsey, Sudip Roy, Jacques Pienaar
PipeMare: Asynchronous Pipeline Parallel DNN Training SysML 2021

Bowen Yang, Jian Zhang, Jonathan Li, Christopher Re, Christopher Aberger, Christopher De Sa
An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems SysML 2021

Ahmed M. Abdelmoniem, Ahmed Elzanaty, Mohamed-Slim Alouini , Marco Canini

Code: t https://github.com/sands-lab/SIDCo
Value Learning for Throughput Optimization of Deep Learning Workloads SysML 2021

Benoit Steiner, Chris Cummins, Horace He, Hugh Leather
Scaling Distributed Training with Adaptive Summation SysML 2021

Saeed Maleki, Madan Musuvathi, Todd Mytkowicz, Olli Saarikivi, Tianju Xu, Vadim Eksarevskiy, Jaliya Ekanayake, Emad Barsoum
Learning on Distributed Traces for Data Center Storage Systems SysML 2021

Giulio Zhou, Martin Maas
Pufferfish: Communication-efficient Models At No Extra Cost SysML 2021

Hongyi Wang, Saurabh Agarwal, Dimitris Papailiopoulos

Code: https://github.com/hwang595/Pufferfish
A Learned Performance Model for Tensor Processing Units SysML 2021

Sam Kaufman, Phitchaya Phothilimthana, Yanqi Zhou, Charith Mendis, Sudip Roy, Amit Sabne, Mike Burrows
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters SysML 2021

Shaohuai Shi, Xianhao Zhou, Shutao Song, Xingyao Wang, Zilin Zhu, Xue Huang, Xinan Jiang, Feihu Zhou, Zhenyu Guo, Liqiang Xie, Rui Lan, Xianbin Ouyang, Yan Zhang, Jieqian Wei, Jing Gong, Weiliang Lin, Ping Gao, Peng Meng, Xiaomin Xu, Chenyang Guo, Bo Yang, Zhibo Chen, Yongjian Wu, Xiaowen Chu
ModularNAS: Towards Modularized and Reusable Neural Architecture Search SysML 2021

Yunfeng Lin, Guilin Li, Xing Zhang, Weinan Zhang, Bo Chen, Ruiming Tang, Zhenguo Li, Jiashi Feng, Yong Yu

Code: https://github.com/huawei-noah/vega/tree/master/vega/algorithms/nas/modnas
FLAML: A Fast and Lightweight AutoML Library SysML 2021

Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu

Code: https://github.com/microsoft/FLAML
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models SysML 2021 D

Chunxing Yin, Bilge Acun, Carole-Jean Wu, Xing Liu

Code: https://github.com/facebookresearch/FBTT-Embedding
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection SysML 2021

Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, Leman Akoglu

Code: https://github.com/yzhao062/SUOD
Pipelined Backpropagation at Scale: Training Large Models without Batches SysML 2021

Atli Kosson, Vitaliy Chiley, Abhinav Venigalla, Joel Hestness, Urs Koster
Fluid: Resource-aware Hyperparameter Tuning Engine SysML 2021

Peifeng Yu, Jiachen Liu, Mosharaf Chowdhury
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers SysML 2021

Colby Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul Whatmough

Code: https://github.com/ARM-software/ML-zoo
Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices SysML 2021

Urmish Thakker, Paul Whatmough, ZHIGANG LIU, Matthew Mattina, Jesse Beu
A Distributed Graph-Theoretic Framework for Automatic Parallelization in Multi-core Systems SysML 2021

Guixiang Ma, Yao Xiao, Theodore Willke, Nesreen Ahmed, Shahin Nazarian, Paul Bogdan
Bit Error Robustness for Energy-Efficient DNN Accelerators SysML 2021

David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele
Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models SysML 2021

Shang Wang, Peiming Yang, Yuxuan Zheng, Xin Li, Gennady Pekhimenko
Characterizing and Taming Model Instability Across Edge Devices SysML 2021

Eyal Cidon, Evgenya Pergament, Zain Asgar, Asaf Cidon, Sachin Katti
Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery SysML 2021

Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu
FirePlace: Placing Firecraker Virtual Machines with Hindsight Imitation SysML 2021

Bharathan Balaji, Christopher Kakovitch, Balakrishnan Narayanaswamy
sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data SysML 2021

Guanhua Wang, Zhuang Liu, Brandon Hsieh, Siyuan Zhuang, Joseph Gonzalez, Trevor Darrell, Ion Stoica

Code: https://github.com/GuanhuaWang/sensAI
Larq Compute Engine: Design, Benchmark and Deploy State-of-the-Art Binarized Neural Networks SysML 2021

Tom Bannink, Adam Hillier, Lukas Geiger, Tim de Bruin, Leon Overweel, Jelmer Neeven, Koen Helwegen

Code: https://github.com/larq/compute-engine
Wavelet: Efficient DNN Training with Tick-Tock Scheduling SysML 2021

Guanhua Wang, Kehan Wang, Kenan Jiang, XIANGJUN LI, Ion Stoica
Data Movement Is All You Need SysML 2021

Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler

Code: https://github.com/spcl/substation
Scaling Polyhedral Neural Network Verification on GPUs SysML 2021

Christoph Müller , François Serre, Gagandeep Singh, Markus Püschel, Martin Vechev

Code: https://github.com/eth-sri/eran
Accounting for Variance in Machine Learning Benchmarks SysML 2021

Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Nazanin Mohammadi Sepahvand, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Tal Arbel, Chris Pal, Gael Varoquaux, Pascal Vincent
Amazon SageMaker Debugger: A System for Real-Time Insights into Machine Learning Model Training SysML 2021

Nathalie Rauschmayr, Vikas Kumar, Rahul Huilgol, Andrea Olgiati, Satadal Bhattacharjee, Nihal Harish, Vandana Kannan, Amol Lele, Anirudh Acharya, Jared Nielsen, Lakshmi Ramakrishnan, Ishan Bhatt, Kohen Chia, Neelesh Dodda, Zhihan Li, Jiacheng Gu, Miyoung Choi, Balajee Nagarajan, Jeffrey Geevarghese, Denis Davydenko, Sifei Li, Lu Huang, Edward Kim, Tyler Hill, Krishnaram Kenthapadi

Code: https://github.com/awslabs/sagemaker-debugger
RL-Scope: Cross-stack Profiling for Deep Reinforcement Learning Workloads SysML 2021

James Gleeson, Moshe Gabel, Gennady Pekhimenko, Eyal de Lara, Srivatsan Krishnan, Vijay Janapa Reddi

Code: https://github.com/UofT-EcoSystem/rlscope
TensorFlow Lite Micro: Embedded Machine Learning for TinyML Systems SysML 2021

Robert David, Jared Duke, Advait Jain, Vijay Janapa Reddi, Nat Jeffries, Jian Li, Nick Kreeger, Ian Nappier, Meghna Natraj, Tiezhen Wang, Pete Warden, Rocky Rhodes
ByzShield: An Efficient and Robust System for Distributed Training SysML 2021

Konstantinos Konstantinidis, Aditya Ramamoorthy

Code: https://github.com/kkonstantinidis/ByzShield
In-network Aggregation for Shared Machine Learning Clusters SysML 2021

Nadeen Gebara, Manya Ghobadi, Paolo Costa
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions SysML 2021

Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li , Jingren Zhou, Ce Zhang, Gustavo Alonso
Accelerate Inference of CNNs for Video Analysis While Preserving Exactness Exploiting Activation Sparsity SysML 2021

Toshiaki Wakatsuki, Sekitoshi Kanai, Yasuhiro Fujiwara
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference SysML 2021

Steve Dai, Rangha Venkatesan, Mark Ren, Brian Zimmer, William Dally, Brucek Khailany
CODE: Compiler-based Neuron-aware Ensemble training SysML 2021

Ettore M. G. Trainiti, Thanapon Noraset, David Demeter, Doug Downey, Simone Campanoni

VLDB

2016

SystemML: Declarative Machine Learning on Spark VLDB 2016

Matthias Boehm, Michael W. Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Arvind C. Surve, Shirish Tatikonda
Compressed linear algebra for large-scale machine learning VLDB 2016

Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald
dmapply: A functional primitive to express distributed machine learning algorithms in R VLDB 2016

Edward Ma, Vishrut Gupta, Meichun Hsu, Indrajit Roy

2017

MLog: Towards Declarative In-Database Machine Learning VLDB 2017

Xupeng Li, Bin Cui, Yiru Chen, Wentao Wu, Ce Zhang

Code: https://github.com/DS3Lab/MLog
Towards Linear Algebra over Normalized Data VLDB 2017

Lingjiao Chen, Arun Kumar, Jeffrey Naughton, Jignesh M. Patel
Distributed Join Algorithms on Thousands of Cores VLDB 2017

Claude Barthels, Ingo Müller, Timo Schneider, Gustavo Alonso, Torsten Hoefler
Probabilistic demand forecasting at scale VLDB 2017

Joos-Hendrik Böse, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias Seeger, Yuyang Wang
Optimizing Deep CNN-Based Queries over Video Streams at Scale VLDB 2017

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

Code: https://github.com/stanford-futuredata/noscope

2018

On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML VLDB 2018

Raj Kumar Maity, Ankit Singh Rawat, Arya MazumdarRaj Kumar Maity, Ankit Singh Rawat, Arya Mazumdar

Code: https://github.com/apache/systemml
Helix: Accelerating Human-in-the-loop Machine Learning VLDB 2018

Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran
Ease.ml in action: towards multi-tenant declarative learning services VLDB 2018

Bojan Karlaš, Ji Liu, Wentao Wu, Ce Zhang
Learning Efficiently Over Heterogeneous Databases VLDB 2018

Jose Picado, Arash Termehchy, Sudhanshu Pathak
MLBench: Benchmarking Machine Learning Services Against Human Experts VLDB 2018

Yu Liu, Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang
Distributed Representations of Tuples for Entity Resolution VLDB 2018

Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq Joty, Mourad Ouzzani, Nan Tang

2020

SKCompress: compressing sparse and nonuniform gradient in distributed machine learning

Jiawei Jiang, Fangcheng Fu, Tong Yang, Yingxia Shao, Bin Cui
PyTorch Distributed: Experiences on Accelerating Data Parallel Training VLDB 2020

Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, Soumith Chintala
Tunable Streaming Graph Embeddings at Scale VLDB 2020

Serafeim Papadias
Snorkel: rapid training data creation with weak supervision

Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason A. Fries, Sen Wu, Christopher Ré

Code: https://github.com/snorkel-team/snorkel/

2021

Model averaging in distributed machine learning: a case study with Apache Spark

Yunyan Guo, Zhipeng Zhang, Jiawei Jiang, Wentao Wu, Ce Zhang, Bin Cui, Jianzhong Li
Towards a Unified Knowledge Graph Data Management System VLDB 2021

Baozhu Liu, Xin Wang, Pengkai Liu, Sizhuo Li
Exploiting Data Distribution in Distributed Learning of Deep Classification Models under the Parameter Server Architecture VLDB 2021

Nikodimos Provatas

SIGCOMM

2019

Learning scheduling algorithms for data processing clusters SIGCOMM 2019

Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, Mohammad Alizadeh
An Edge Computing Marketplace for Distributed Machine Learning SIGCOMM 2019

Susham Yerabolu, Samuel Gomena, Ehsan Aryafar, Carlee Joe-Wong
Challenging the generalization capabilities of Graph Neural Networks for network modeling SIGCOMM 2019

José Suárez-Varela, Sergi Carol-Bosch, Krzysztof Rusek, Paul Almasan, Marta Arias, Pere Barlet-Ros, Albert Cabellos-Aparicio

Code: https://github.com/knowledgedefinednetworking/demo-routenet

2020

Is Network the Bottleneck of Distributed Training? SIGCOMM 2020

Zhen Zhang, Chaokun Chang, Haibin Lin, Yida Wang, Raman Arora, Xin Jin

Code: https://github.com/netx-repo/training-bottleneck
Challenges in Using ML for Networking Research: How to Label If You Must SIGCOMM 2020

Yukhe Lavinia, Ramakrishnan Durairajan, Reza Rejaie, Walter Willinger

2021

Hoplite: efficient and fault-tolerant collective communication for task-based distributed systems SIGCOMM 2021

Siyuan Zhuang, Zhuohan Li, Danyang Zhuo, Stephanie Wang, Eric Liang, Robert Nishihara, Philipp Moritz, Ion Stoica

Code: https://github.com/suquark/hoplite
SiP-ML: high-bandwidth optical network interconnects for machine learning training SIGCOMM 2021

Mehrdad Khani Shirkoohi, Manya Ghobadi, Mohammad Alizadeh, Ziyi Zhu, Madeleine Glick, Keren Bergman, Amin Vahdat, Benjamin Klenk, Eiman Ebrahimi

Code: https://github.com/MLNetwork/rostam

ICDE

2016

Being prepared in a sparse world: The case of KNN graph construction ICDE 2016

Antoine Boutet, Anne-Marie Kermarrec, Nupur Mittal, François Taïani
pSCAN: Fast and exact structural graph clustering ICDE 2016

Lijun Chang, Wei Li, Xuemin Lin, Lu Qin, Wenjie Zhang

2017

Scalable and Interactive Graph Clustering Algorithm on Multicore CPUs ICDE 2017

Son T. Mai, Martin Storgaard Dieu, Ira Assent, Jon Jacobsen, Jesper Kristensen, Mathias Birk
Towards Unified Data and Lifecycle Management for Deep Learning ICDE 2017

Hui Miao, Ang Li, Larry S. Davis, Amol Deshpande
In-Memory Distributed Matrix Computation Processing and Optimization ICDE 2017

Yongyang Yu, Mingjie Tang, Walid G. Aref, Qutaibah M. Malluhi, Mostafa M. Abbas, Mourad Ouzzani

Code: https://github.com/yuyongyang800/SparkDistributedMatrix
Modeling Scalability of Distributed Machine Learning ICDE 2017

Alexander Ulanov, Andrey Simanovsky, Manish Marwah
ModelHub: Deep Learning Lifecycle Management ICDE 2017

Hui Miao, Ang Li, Larry S. Davis, Amol Deshpande

2018

Aurum: A Data Discovery System ICDE 2018

Raul Castro Fernandez, Ziawasch Abedjan, Famien Koko, Gina Yuan, Samuel Madden, Michael Stonebraker

2019

MLlib*: Fast Training of GLMs Using Spark MLlib ICDE 2019

Zhipeng Zhang, Jiawei Jiang, Wentao Wu, Ce Zhang, Lele Yu, Bin Cui
Towards Explaining the Effects of Data Preprocessing on Machine Learning ICDE 2019

Carlos Vladimiro Gonzalez Zelaya
Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study ICDE 2019

Guowei Xu, Wenbiao Ding, Jiliang Tang, Songfan Yang, Gale Yan Huang, Zitao Liu

Code: https://github.com/tal-ai/Representation-Learning-with-crowdsourced-Labels
CogLearn: A Cognitive Graph-Oriented Online Learning System ICDE 2019

Yang Pian, Yu Lu, Penghe Chen, Qinglong Duan
Don't Fear the REAPER: A Framework for Materializing and Reusing Deep-Learning Models ICDE 2019

Melanie B. Sigl
Adaptive Deep Reuse: Accelerating CNN Training on the Fly ICDE 2019

Lin Ning, Hui Guan, Xipeng Shen

2020

ColumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent ICDE 2020

Zhipeng Zhang, Wentao Wu, Jiawei Jiang, Lele Yu, Bin Cui, Ce Zhang
PSGraph: How Tencent trains extremely large-scale graphs with Spark ICDE 2020

Jiawei Jiang, Pin Xiao, Lele Yu, Xiaosen Li, Jiefeng Cheng, Xupeng Miao, Zhipeng Zhang, Bin Cui

Code: https://github.com/Angel-ML/angel
Efficient Diversity-Driven Ensemble for Deep Neural Networks ICDE 2020

Wentao Zhang, Jiawei Jiang, Yingxia Shao, Bin Cui
HomoPAI: A Secure Collaborative Machine Learning Platform based on Homomorphic Encryption ICDE 2020

Qifei Li, Zhicong Huang, Wen-jie Lu, Cheng Hong, Hunter Qu, Hui He, Weizhe Zhang
Machine Learning Meets Big Spatial Data ICDE 2020

Ibrahim Sabek, Mohamed F. Mokbel
On the Integration of Machine Learning and Array Databases ICDE 2020

Sebastián Villarroya, Peter Baumann
Fela: Incorporating Flexible Parallelism and Elastic Tuning to Accelerate Large-Scale DML ICDE 2020

Jinkun Geng, Dan Li, Shuai Wang

2021

CuWide: Towards Efficient Flow-based Training for Sparse Wide Models on GPUs (Extended Abstract) ICDE 2021

Xupeng Miao, Lingxiao Ma, Zhi Yang, Yingxia Shao, Bin Cui, Lele Yu, Jiawei Jiang

Code: https://github.com/DMALab/cuWide
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks ICDE 2021

Peng Li, Xi Rao, Jennifer Blase, Yue Zhang, Xu Chu, Ce Zhang

Code: https://github.com/chu-data-lab/CleanML
MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines ICDE 2021

Zhaojing Luo, Sai Ho Yeung, Meihui Zhang, Kaiping Zheng, Lei Zhu, Gang Chen, Feiyi Fan, Qian Lin, Kee Yuan Ngiam, Beng Chin Ooi
Ranking Data Slices for ML Model Validation: A Shapley Value Approach ICDE 2021

Eitan Farchi, Ramasuri Narayanam, Lokesh Nagalapatti

SIGKDD

2017

TFX: A TensorFlow-Based Production-Scale Machine Learning Platform SIGKDD 2017

Denis Baylor, Eric Breck, Heng-Tze Cheng, Noah Fiedel, Chuan Yu Foo, Zakaria Haque, Salem Haykal, Mustafa Ispir, Vihan Jain, Levent Koc, Chiu Yuen Koo, Lukasz Lew, Clemens Mewald, Akshay Naresh Modi, Neoklis Polyzotis, Sukriti Ramesh, Sudip Roy, Steven Euijong Whang, Martin Wicke, Jarek Wilkiewicz, Xin Zhang, Martin Zinkevich
PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification SIGKDD 2017

Ian E.H. Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit Dhillon, Eric Xing
A Data Mining Framework for Valuing Large Portfolios of Variable Annuities SIGKDD 2017

Guojun Gan, Jimmy Xiangji Huang
Google Vizier: A Service for Black-Box Optimization SIGKDD 2017

Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, D. Sculley
KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial SIGKDD 2017

Jun Zhou, Xiaolong Li, Peilin Zhao, Chaochao Chen, Longfei Li, Xinxing Yang, Qing Cui, Jin Yu, Xu Chen, Yi Ding, Yuan Alan Qi

2018

Deploying Machine Learning Models for Public Policy: A Framework SIGKDD 2018

*Klaus Ackermann, Joe Walsh, Adolfo De Unánue, Hareem Naveed, Andrea Navarrete Rivera, Sun-Joo Lee, Jason Bennett, Michael Defoe, Crystal Cody, Lauren Haynes, Rayid Ghani *
Scalable Optimization for Embedding Highly-Dynamic and Recency-Sensitive Data SIGKDD 2018

Xumin Chen, Peng Cui, Lingling Yi, Shiqiang Yang
Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning SIGKDD 2018

Patrick Koch, Oleg Golovidov, Steven Gardner, Brett Wujek, Joshua Griffin, Yan Xu
Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale SIGKDD 2018

Peter W J Staar, Michele Dolfi, Christoph Auer, Costas Bekas

2019

FDML: A Collaborative Machine Learning Framework for Distributed Features SIGKDD 2019

Yaochen Hu, Di Niu, Jianming Yang, Shengping Zhou
Machine Learning at Microsoft with ML.NET SIGKDD 2019

Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupré, Vadim Eksarevskiy, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu

Code: https://github.com/dotnet/machinelearning
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks SIGKDD 2019

Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh

Code: https://github.com/benedekrozemberczki/ClusterGCN
Training and Meta-Training Binary Neural Networks with Quantum Computing SIGKDD 2019

Abdulah Fawaz, Paul Klein, Sebastien Piat, Simone Severini, Peter Mountney
A Generalized Framework for Population Based Training SIGKDD 2019

Ang Li, Ola Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, Pramod Gupta
Large-Scale Training Framework for Video Annotation SIGKDD 2019

Seong Jae Hwang, Joonseok Lee, Balakrishnan Varadarajan, Ariel Gordon, Zheng Xu, Apostol Natsev
OBOE: Collaborative Filtering for AutoML Model Selection SIGKDD 2019

Chengrun Yang, Yuji Akimoto, Dae Won Kim, Madeleine Udell

Code: https://github.com/udellgroup/oboe

2020

Building Continuous Integration Services for Machine Learning SIGKDD 2020

Bojan Karlas, Matteo Interlandi, Cédric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer
An Empirical Analysis of Backward Compatibility in Machine Learning Systems SIGKDD 2020

Megha Srivastava, Besmira Nushi, Ece Kamar, Shital Shah, Eric Horvitz
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems SIGKDD 2020

Khalil Muhammad, Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Neil Hurley, James Geraci, Aonghus Lawlor
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks SIGKDD 2020

Weilin Cong, Rana Forsati, Mahmut T. Kandemir, Mehrdad Mahdavi
GPT-GNN: Generative Pre-Training of Graph Neural Networks SIGKDD 2020

Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun

Code: https://github.com/acbull/GPT-GNN
Large-Scale Training System for 100-Million Classification at Alibaba SIGKDD 2020

Liuyihan Song, Pan Pan, Kang Zhao, Hao Yang, Yiming Chen, Yingya Zhang, Yinghui Xu, Rong Jin
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering SIGKDD 2020

Yuval Heffetz, Roman Vainshtein, Gilad Katz, Lior Rokach
AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space SIGKDD 2020

Chengrun Yang, Jicong Fan, Ziyang Wu, Madeleine Udell

Code: https://github.com/udellgroup/oboe

2021

DeGNN: Improving Graph Neural Networks with Graph Decomposition SIGKDD 2021

Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Susie Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang
ROD: Reception-aware Online Distillation for Sparse Graphs SIGKDD 2021

Wentao Zhang, Yuezihan Jiang, Yang Li, Zeang Sheng, Yu Shen, Xupeng Miao, Liang Wang, Zhi Yang, Bin Cui
OpenBox: A Generalized Black-box Optimization Service SIGKDD 2021

Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, Ce Zhang, Bin Cui

Code: https://github.com/PKU-DAIR/open-box
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud SIGKDD 2021

Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Ankit A. Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi

Code: https://github.com/aws/amazon-sagemaker-clarify
AutoSmart: An Efficient and Automatic Machine Learning Framework for Temporal Relational Data SIGKDD 2021

Zhipeng Luo, Zhixing He, Jin Wang, Manqing Dong, Jianqiang Huang, Mingjian Chen, Bohang Zheng

Code: https://github.com/DeepBlueAI/AutoSmart
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs SIGKDD 2021

Jialin Dong, Da Zheng, Lin F. Yang, George Karypis
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism SIGKDD 2021

Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney
Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters SIGKDD 2021

Yuzhen Huang, Xiaohan Wei, Xing Wang, Jiyan Yang, Bor-Yiing Su, Shivam Bharuka, Dhruv Choudhary, Zewei Jiang, Hai Zheng, Jack Langman

EuroSys

2016

STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning EuroSys 2016

Jin Kyu Kim, Qirong Ho, Seunghak Lee, Xun Zheng, Wei Dai, Garth A. Gibson, Eric P. Xing
GeePS: Scalable Deep Learning on Distributed GPUs with a GPU-specialized Parameter Server EuroSys 2016

Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing

2017

Proteus: Agile ML Elasticity Through Tiered Reliability in Dynamic Resource Markets EuroSys 2017

Aaron Harlap, Alexey Tumanov, Andrew Chung, Gregory R. Ganger, Phillip B. Gibbons

2018

Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning Clusters EuroSys 2018

Yangrui Chen, Yanghua Peng, Yixin Bao, Chuan Wu, Yibo Zhu, Chuanxiong Guo
Dynamic Control Flow in Large-scale Machine Learning EuroSys 2018

Yuan Yu, Martín Abadi, Paul Barham, Eugene Brevdo, Mike Burrows, Andy Davis, Jeff Dean, Sanjay Ghemawat, Tim Harley, Peter Hawkins, Michael Isard, Manjunath Kudlur, Rajat Monga, Derek Murray, Xiaoqiang Zheng
Improving the Expressiveness of Deep Learning Frameworks with Recursion EuroSys 2018

Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun
Low Latency RNN Inference with Cellular Batching EuroSys 2018

Pin Gao, Lingfan Yu, Yongwei Wu, Jinyang Li

2019

GRNN: Low-Latency and Scalable RNN Inference on GPUs EuroSys 2019

Connor Holmes, Daniel Mawhirter, Yuxiong He, Feng Yan, Bo Wu

Code: https://github.com/cmikeh2/grnn
Automating Dependence-Aware Parallelization of Machine Learning Training on Distributed Shared Memory EuroSys 2019

Jinliang Wei, Garth A. Gibson, Phillip B. Gibbons, Eric P. Xing
Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks EuroSys 2019

Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

Code: https://github.com/snuspl/parallax
Fast Distributed Deep Learning over RDMA EuroSys 2019

Jilong Xue, Youshan Miao, Cheng Chen, Ming Wu, Lintao Zhang, Lidong Zhou
μLayer: Low Latency On-Device Inference Using Cooperative Single-Layer Acceleration and Processor-Friendly Quantization EuroSys 2019

Youngsok Kim, Joonsung Kim, Dongju Chae, Daehyun Kim, Jangwoo Kim

2020

Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning EuroSys 2020

Shubham Chaudhary, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Srinidhi Viswanatha
Subway: minimizing data transfer during out-of-GPU-memory graph processing EuroSys 2020

Amir Hossein Nodehi Sabet, Zhijia Zhao, Rajiv Gupta

Code: https://github.com/AutomataLab/Subway
Peregrine: a pattern-aware graph mining system EuroSys 2020

Kasra Jamshidi, Rakesh Mahadasa, Keval Vora

Code: https://github.com/pdclab/peregrine

2021

FlexGraph: a flexible and efficient distributed framework for GNN training EuroSys 2021

Lei Wang, Qiang Yin, Chao Tian, Jianbang Yang, Rong Chen, Wenyuan Yu, Zihang Yao, Jingren Zhou
DGCL: an efficient communication library for distributed GNN training EuroSys 2021

Zhenkun Cai, Xiao Yan, Yidi Wu, Kaihao Ma, James Cheng, Fan Yu

Code: https://github.com/czkkkkkk/gccl
Accelerating graph sampling for graph machine learning using GPUs EuroSys 2021

Abhinav Jangda, Sandeep Polisetty, Arjun Guha, Marco Serafini
Tahoe: tree structure-aware high performance inference engine for decision tree ensemble on GPU EuroSys 2021

Zhen Xie, Wenqian Dong, Jiawen Liu, Hang Liu, Dong Li

Code: https://github.com/zhen-xie/Tahoe
Seastar: Vertex-Centric Programming for Graph Neural Networks EuroSys 2021

Yidi Wu, Kaihao Ma, Zhenkun Cai, Tatiana Jin, Boyang Li, Chengguang Zheng, James Cheng, Fan Yu

SoCC

2016

Ako: Decentralised Deep Learning with Partial Gradient Exchange SoCC 2016

Pijika Watcharapichat, Victoria Lopez Morales, Raul Castro Fernandez, Peter Pietzuch
Addressing the straggler problem for iterative convergent parallel ML SoCC 2016

Aaron Harlap, Henggang Cui, Wei Dai, Jinliang Wei, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing

2017

SLAQ: quality-driven scheduling for distributed machine learning SoCC 2017

Haoyu Zhang, Logan Stafman, Andrew Or, Michael J. Freedman
How good are machine learning clouds for binary classification with good features?: extended abstract SoCC 2017

Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang
SQML: large-scale in-database machine learning with pure SQL SoCC 2017

Umar Syed, Sergei Vassilvitskii

2018

Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training SoCC 2018

Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy
Scheduling CPU for GPU-based Deep Learning Jobs SoCC 2018

Wencong Xiao, Zhenhua Han, Hanyu Zhao, Xuan Peng, Quanlu Zhang, Fan Yang, Lidong Zhou
SSD QoS Improvements through Machine Learning SoCC 2018

Chandranil Chakraborttii, Vikas Sinha, Heiner Litz

2019

Training Deep Neural Networks Using Posit Number System SoCC 2019

Jinming Lu, Siyuan Lu, Zhisheng Wang, Chao Fang, Jun Lin, Zhongfeng Wang, Li Du
An Operation-Minimized FPGA Accelerator Design by Dynamically Exploiting Sparsity in CNN Winograd Transform SoCC 2019

Xinkai Di, Haigang Yang, Zhihong Huang, Ning Mao

2020

A DNN Compression Framework for SOT-MRAM-based Processing-In-Memory Engine SoCC 2020

Geng Yuan, Xiaolong Ma, Sheng Lin, Zhengang Li, Jieren Deng, Caiwen Ding
Cycle-to-cycle Variation Enabled Energy Efficient Privacy Preserving Technology in ANN SoCC 2020

Jingyan Fu, Zhiheng Liao, Jinhui Wang
Improving the Performance of a NoC-based CNN Accelerator with Gather Support SoCC 2020

Binayak Tiwari, Mei Yang, Xiaohang Wang, Yingtao Jiang, Venkatesan Muthukumar
Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer SoCC 2020

Siyuan Lu, Meiqi Wang, Shuang Liang, Jun Lin, Zhongfeng Wang
Optimizing CNN Accelerator With Improved Roofline Model SoCC 2020

Shaoxia Fang, Shulin Zeng, Yu Wang
AutoML for Multilayer Perceptron and FPGA Co-design SoCC 2020

Philip Colangelo, Oren Segal, Alexander Speicher, Martin Margala

WWW

2018

Sharing Deep Neural Network Models with Interpretation WWW 2018

Huijun Wu, Chen Wang, Jie Yin, Kai Lu, Liming Zhu

INFOCOM

2017

AccurateML: Information-aggregation-based approximate processing for fast and accurate machine learning on MapReduce INFOCOM 2017

Rui Han, Fan Zhang, Zhentao Wang
A Secure and Verifiable Outsourcing Scheme for Matrix Inverse Computation INFOCOM 2017

Chunqiang Hu, Abdulrahman Alhothaily, Arwa Alrawais, Xiuzhen Cheng, Carl Sturtivant, Hang Liu

2018

When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning INFOCOM 2018

Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, Kevin Chan
Online Job Scheduling in Distributed Machine Learning Clusters INFOCOM 2018

Yixin Bao, Yanghua Peng, Chuan Wu, Zongpeng Li
DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics INFOCOM 2018

Xukan Ran, Haolianz Chen, Xiaodan Zhu, Zhenming Liu, Jiasi Chen
FML: Fast Machine Learning for 5G mmWave Vehicular Communications INFOCOM 2018

Arash Asadi, Sabrina Müller, Gek Hong Sim, Anja Klein, Matthias Hollick

Contributors

Contributed by Xupeng Miao, Zihao Yu, Chunan Shi and Hetu team members.

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Awesome-Machine-Learning-System-Papers

Conference

Category Keywords

Papers

OSDI

2016

2018

2020

2021

SOSP

2017

2019

2021

SIGMOD

2016

2017

2018

2019

2020

2021

ASPLOS

2017

2018

2019

2020

2021

ATC

2017

2018

2019

2020

2021

PPoPP

2017

2018

2019

2020

2021

HPCA

2017

2018

2019

2020

2021

MICRO

2017

2018

2019

2020

2021

SC

2016

2018

2019

2020

2021

NSDI

2017

2019

2021

ISCA

2017

2018

2019

2020

2021

SysML

2018 (Session only)

2019

2020

2021

VLDB

2016

2017

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