Last release supporting python 3.8 and Tensorflow 2.10 to 2.12, next release will support python 3.9 to 3.11 and Tensorflow 2.15.

Update QuantizeML to version 0.10.1

New features

Introduced "analysis" module that allows to gather information about quantized layers and display quantization error informations
Implemented partial quantization that allows to stop at a given layer
Bias quantization improved in layers to handle outliers
Added support for '+' operation quantization in Tensorflow/Keras
Now handling ONNX models in "load_model"
Removed the deprecated 'check_quantization' API and CLI
Added dispatch for QTensor slicing
Added support for ReLU activation comming after an Add layer
Added support for Keras layers defined with an activation parameter
Updated ONNX sanitize function to handle MobileNetV1/V2 ops patterns
Optimized input shift quantization is ONNX skip connections
Added support for QuantizedConcatenate, QuantizedTransposedConvolution, QuantizedRescaling in ONNX

Fixed a cross-layer equalization issue with Conv2DTranspose
Prevented cross-layer equalization when layers do not perform an output scaling operation
Fixed a quantization issue with ONNX where models with skip connection and computation in both branches would lead to an invalid graph
Fixed a issue preventing to load quantized model into a multi-GPU setup
Fixed issues in sanitize when model comes with skip connections both on the Keras and ONNX paths
OutputQuantizer will now properly use their buffer_bitwidth parameter

Aligned with FPGA 1492/1515(ViT)

[akida] Added a mapping mode to akida.Model.map that allows to map a model on AllNps (default), HwPr (all NPs and partial reconfig) or Minimal (legacy mode using less resources)
[akida] Added a 'run' CLI to perform an inference on device from a one line command
[akida] Added support for multi-sequence mapping for V2
[akida] Full support of Stem layer in V2 HW
[akida] Skip connection support in V2: added mesh discovery and early mapping algorithm
[akida] Model constructor that takes a list of layers as parameter now supports Add layers
[akida] Added support for CNP without activation but with max pooling enabled
[akida] When mapping is not possible after a search, the initial mapping is restored
[cnn2snn] Bumped QuantizeML requirement to 0.10.1 and included support for Add>ReLU conversion

[akida] Added an exception when unsigned inputs are fed to a V2 InputData
[akida] Updated MeshMapper behavior for DepthwiseConvolution layers
[akida] Restricted FNP > CNP mapping that is not supported
[akida] Fixed non-homogenous behavior in the mapping seach algorithm depending on the hw_only parameter

Aligned with Akida/CNN2SNN 2.8.1 and QuantizeML 0.10.1
Improved AkidaNet18/CenterNet detection model training and architecture
Updated a large part of the 8-bits models from the zoo with better bias quantization (and accuracy)
Updated GXNOR/MNIST model quantization scheme
Fixed AkidaUNet evaluation pipeline
Added explicit dependency to tqdm
Improved exception handling in load_model

Added akida run CLI usage in Akida user guide
Added akida_models summary CLI usage in Akida models zoo user guide
Added analysis module in QuantizeML user guide
Reworked advanced ONNX quantization example that uses custom pattern to reflect updated supported patterns