Qonnx

Highlights:
* Channels-last conversion improvements and support for branching topologies (148 149 163)
* New rounding modes in `Quant` (134)
* QuantLSTM QCDQ representation (79)
* Inference cost breakdown (101)
* Channel pruning and quantization utilities (71 94)

Full list of merged PRs:
https://github.com/fastmachinelearning/qonnx/milestone/4?closed=1

Highlights:

- Initial support for converting between Q(C)DQ and Quant styles of quantization (48 , 56 , 65 )
- Improvements to `qonnx-exec` for faster QONNX model execution and accuracy validation (64 )
- Improvements to constant folding and subgraph execution (47 )
- Improvements to inference cost measurement (53 , 55 )

Full list of merged PRs:
https://github.com/fastmachinelearning/qonnx/milestone/3?closed=1

What's new in this release
* Support exporting QONNX from QKeras (28 )
* Support exporting QONNX from HAWQ (41)
* Tensor attributes in custom op wrappers (24)
* Use knowledge of weight matrices to further optimize accumulator range calculation (29)
* Support spatial -> channel rebalancing for certain convolutional kernels (22)
* Numerous bugfixes and other improvements - [see full list of merged PRs for this release](https://github.com/fastmachinelearning/qonnx/milestone/2?closed=1)