Deep Compression for PyTorch Model Deployment on Microcontrollers

In this repository, you can find the source code of the paper "Deep Compression for PyTorch Model Deployment on Microcontrollers".

This work follows the paper Efficient Neural Network Deployment for Microcontroller by Hasan Unlu. You can find the repository of the source code of that paper here.

Dependencies

These are the only versions tested; therefore, other versions may be incompatible.

• Python 3.8.5
• PyTorch 1.8
• Tensorboard 2.4.1
• Neural Network Intelligence (NNI) 2.1

Usage

Running the generator.py will generate main.c and main.h files in the outputs folder. helper_functions.h file is required by main.c.

Only two network architectures are included in this generator. To switch between the two, change this line in generator.py:

dataset_name = 'mnist' # change this for different networks. can be 'mnist' or 'cifar10'

Some networks might be sensitive to input activation quantization. To disable input quantization, change this line in generator.py:

quantize_input = dataset_name != 'cifar10' # change this for input quantization. can be True or False

If your network is pre-trained you can disable initial training. Your pre-trained network should be in the saves folder with the correct name (original.pt). To disable initial training, change this line in generator.py:

pre_trained = True # change this if your model is pre-trained. can be True or False

If your GPU supports CUDA, you can enable CUDA usage to speedup the process. CPU will be used if CUDA is not enabled. To use CUDA, change this line in generator.py:

use_cuda = False # change this if yor GPU supports CUDA. can be True or False

You can also use this generator with networks other than LeNet-5 or the CIFAR-10 test network implemented. You need to create the network using PyTorch building blocks and adjust the optimizer. Supported PyTorch building blocks are:

• Conv2d
• MaxPool2d
• Linear
• Flatten
• ReLU

You can look at our LeNet-5 implementation as a reference to supported model implementations:

import torch.nn as nn

nn.Sequential (
    nn.Conv2d(3, 32, kernel_size=5, stride=1, padding=2),
    nn.ReLU(),

    nn.MaxPool2d(kernel_size=2, stride=2, padding=0),

    nn.Conv2d(32, 16, kernel_size=5, stride=1, padding=2),
    nn.ReLU(),

    nn.MaxPool2d(kernel_size=2, stride=2, padding=0),

    nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
    nn.ReLU(),

    nn.MaxPool2d(kernel_size=2, stride=2, padding=0),

    nn.Flatten(),

    nn.Linear(4*4*32, 10),
)