Draft: [New] first conv with cmsis_nn working

Context

In the context of the integration of cmsis_nn for aidge. This merge request is a first version focusing on the ConvScaling metoperator.

Modified files

• operator.py : Now using aidge_quantization.Quantizer instead of aidge_core.scaling. Only including "arm_nn_functions.h" and copying kernels in export.py. Using my own function to compute fixed_point shift and multiplier;
• export.py, now copying folder of cmsis_nn and two options between "aidge_arm" or "cmsis_nn";
• arm_cmsis_nn_conv2d.jinja, both for configuration and forward call with debug options

TODO

• Dataformat different for CMSIS_NN (HWC), need to change ImplSpec and apply recipes
• Only one _mem_offset.jinja, _meminfo.jinja and _def_io.jinja
• Other Kernels (Max/AvgPooling, Relu, FC, PaddedConv, DW) that are available in CMSIS_NN library
• Good method to choose the dtype

assigned to @wboussella

requested review from @cmoineau

marked this merge request as draft

mentioned in merge request !5 (closed)

added 1 commit

• cff2ffbe - export_registry.py : adding an init for ExportLib

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added 22 commits

• cff2ffbe...b920a41f - 18 commits from branch eclipse/aidge:dev
• 467eb8ea - Merge branch 'dev' into 'main'
• 5a253419 - Merge branch 'dev' into 'main'
• 52fdeccc - UPD: version 0.1.0 -> 0.1.1
• 2ff196bd - Merge

Compare with previous version

currently adapt to backend is not working which block the goals of this MR. I suggest to do it without adapt_to_backend by transposing parameters while exporting like it's done in export_cpp, and by adding a transpose node for the first input. It's allow me to confirm my work on templates, and in a future version it will be easy to use it with adapt to backend. @cmoineau @olivierbichler

What is the non working behavior of adaptToBackend?

[ERROR] - Assertion failed: mImpl != nullptr in /local2/is148265/wb274724/STM32_dev/dev/superpoint/aidge_may/aidge/aidge/aidge_core/src/operator/Operator.cpp:64
[FATAL] - resetConsummerProducer(): an implementation is required for ReLU!
Traceback (most recent call last):
  File "/local2/is148265/wb274724/STM32_dev/dev/superpoint/aidge_may/aidge/aidge/aidge_export_arm_cortexm/ba.py", line 201, in <module>
    scheduler = aidge_core.SequentialScheduler(model)
RuntimeError: resetConsummerProducer(): an implementation is required for ReLU!

The problem is that i connot generate scheduling with a model after an adapt to backend containing metaop in this case i haven't a ReLU in my model only a metaop named PaddedConvScalingRelu which is defined in operator.py from aidge_export_arm_cortexm

Seems like a minor bug, reserConsummerProducer must have a bad behavior if MetaOp has an implementation, it shouldn't call resetConsummerProducer on the sub graph.

added StatusDelayed label

added StatusWork in Progress label and removed StatusDelayed label