Update Akida and CNN2SNN to version 2.7.2

New features

[akida] Full support of standard and depthwise transposed convolution in V2 hardware
[akida] Initial support of Stem in V2 hardware (not all used cased covered for now)
[akida] Introduced VitEncoderBlock layer: it corresponds to the groups of layers that will be mapped to an hardware block
[akida] Limited multi-sequence mapping on V2
[akida] Engine fully supports 8-bits outputs for Akida 2.0 (with/without activation)
[akida] Added a workaround to avoid race condition when NPs try to reach AeDMA
[akida] Added support for stride 2 1x1 Conv2D (pointwise) layers for ResNet50
[akida] Added support for 7x7, stride 2 with max pooling (pool_size=3, pool_stride=2) InputConv2D layer for ResNet50
[akida] Added an 'activation' parameter to Add layer for ResNet50
[akida] Improved NP count in 'Model.summary()' for 2.0 components
[akida] Improved layers docstrings
[cnn2snn] A set of layers that are compatible with a VitEncoderBlock will now be converted to the block instead of independent layers

[akida] Fixed and added restriction on successive mappings call that would lead in graph being invalidated and summary to crash
[akida] Fixed StatefulRecurrent fixture and variables ranges
[akida] Fixed clock_counter_en_ initialization
[akida] Fixed padding scheme for pointwise layers
[akida] Fixed an error when generating a trace with a Dequantizer layer
[akida] Fixed MadNorm layer corner case computation errors

Aligned with CNN2SNN 2.7.2
Reworked the detection tools: dataset management now based on TensorFlow datasets, others tools updated accordingly, COCO dataset added
Updated AkidaNet YOLO/VOC model: now trained on COCO and transferred on the whole 20 VOC classes
Introduced AudioViT/Urbansound model, leveraging ViT backbone for audio classification
Added a 'summary' CLI to akida_models that handles Keras, ONNX and Akida models
Rebased DVSSamsung model architecture to its DVSGesture sibling for hardware efficiency
Pruned DS-CNN/KWS model from unnecessary reshape operations

Performances: added AudioViT, updated dectection and audio sections
Updated "Advanced ONNX model quantization" removing parts for patterns that are now supported