The objective of this master thesis is to explore the different techniques used to reduce the resource consumptions of CNNs at inference. Due to GPU-ressource limitations, we limited ourselves to use the CIFAR-10 dataset in order to compare the different networks. The techniques investigated were: 1. Base CNN architecture (WideResnet, ShuffleNet, EffNet, CondenseNet, MobileNet,...) and their related parameters (depth, width, bottlenecks,...) 2. Number of channels per layers (fisher pruning, morphnet and NetAdapt algorithms)
- CondenseNet: clone of https://github.com/ShichenLiu/CondenseNet with some minor modifications to make it run
- morph_net: modification of the code of the paper "[MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks][http://arxiv.org/abs/1711.06798]"
- NetAdapt: self-made implementation of the paper "[NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications][http://arxiv.org/abs/1804.03230]", several modifications were made (the main ones are: fisher pruning instead of weights-norm pruning and training from scratch rather than long term fine-tuning)
- pytorch-prunes: modification of the code of the paper "[Pruning neural networks: is it time to nip it in the bud?][https://arxiv.org/abs/1810.04622]"
- schemes: a folder containing the .odf file I used to create custom schemes to illustrate the different neural networks architectures
- training_from_scratch: a repo containing the code that trains neural networks in tensorflow to then load them as .tflite models on the rasberry-pi
- The cross-compiled tflite 1.13.1 binary as well as the "benchmark" binary that are used for benchmarking.
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