added docs for converter

README.md

@@ -101,6 +101,10 @@ Inference cost for CNV_2W2A.onnx
 
 You can read more about the BOPS metric in [this paper](https://www.frontiersin.org/articles/10.3389/frai.2021.676564/full), Section 4.2 Bit Operations.
 
+### Qkeras to Qonnx Converter
+
+To see details about the qkeras converter and potential limitations check this [document](docs/qkeras-converter/qkeras_to_qonnx.md)
+
 ### Development
 
 Install in editable mode in a venv:

docs/qkeras-converter/qkeras_to_qonnx.md

@@ -0,0 +1,23 @@
+### <a name="Qkeras to Qonnx"></a><a name="abs">**Qkeras to Qonnx**</a>
+
+The converter works by (1) strip QKeras model of quantization attributes and store in a dictionary; (2) convert (as if plain Keras model) using tf2onnx; (3) Insert “Quant” nodes at appropriate locations based on a dictionary of quantization attributes.
+
+The current version has few issues given how tf2onnx inserts the quant nodes. These problems have suitable workarounds detailed below.
+
+### Quantized-Relu
+The quantized-relu quantization inserts a redundant quantization node when used as output activation of Dense/Conv2D layer.
+
+Workaround: Only use quantized-relu activation in a seperate QActivation layers.
+
+<img src="https://user-images.githubusercontent.com/31563706/209125992-e03078e4-ec92-4796-982f-2a31292687d6.png"  width="300" height="500">
+
+### Quantized-Bits
+The quantized-bits quantization node is not added to the model when used in QActivation layers.
+
+Workaround: Use quantized-bits only at the output of a Dense/Conv2D layers.
+
+<img src="https://user-images.githubusercontent.com/31563706/209126623-9956ecea-748e-4d7c-930c-d46d06ab6a14.png"  width="350" height="350">
+
+### Ternary Quantization
+A threshold of 0.5 must be used when using ternary quantization.
+(This is sometimes unstable even with t=0.5)