Upgrade to Quantizeml 0.17.1, Akida/CNN2SNN 2.14.0 and Akida models 1.8.0

Update QuantizeML to version 0.17.1

New features

Now handling models with dynamic shapes in both Keras and ONNX. Shape is deduced from calibration samples or from the input_shape parameter.
Added a Keras and ONNX common reset_buffers entry point for spatiotemporal models
GlobalAveragePooling output will now be quantized to QuantizationParams.activation_bits instead of QuantizationParams.output_bits when preceeded by an activation

Applied reset_buffers to variables recording to prevent shape issue when converting a model to Akida
Handle ONNX models with shared inputs that would not quantize or convert properly
Handle unsupported strides when converting an even kernel to odd
Fixed analysis module issue when applied to TENNs models
Fixed analysis module weight quantization error on Keras models
Keras set_model_shape will now handle tf.dataset samples
It is now possible to quantize a model with a split layer as input

[cnn2snn] Updated requirement to QuantizeML 0.17.0
[akida] Added support for 4-bit in 2.0. Features aligned with 1.0, that is InputConv2D, Conv2D, DepthwiseConv2D and Dense layers support 4-bit weights (except InputConv2D) and activations.
[akida] Extented TNP_B support to 2048 channels and filters
[akida] HRC is now optional in a virtual device
[akida] For real device, input and weight SRAM values are now read from the mesh
[akida] Introduce an akida.NP.SramSize object to manage default memories
[akida] Extended python Layer API with "is_target_component(NP.type)" and "macs"
[akida] Added "akida.compute_minimal_memory" helper

[akida] Fixed several issues when computing input or weight memory sizes for layers

Updated QuantizeML dependency to 0.17.0 and CNN2SNN to 2.14.0
Updated 4-bit models for 2.0 and added a bitwidth parameter to the pretrained helper
TENNs EyeTracking is now evaluated on the labeled test set
Dropped MACS computation helper and CLI: MACS are natively available on Akida layers and models.