This repository contains several PyTorch examples demonstrating fixed-point quantization of deep neural networks, quantization-aware training, bit errors in quantized weights and random bit error training:
- Simple Fixed-Point Quantization for DNNs in PyTorch
- 4.5% Test Error on CIFAR10 with 4-Bit Fixed-Point Quantization
- Implementing Fast Bitwise Operations for PyTorch
- Testing Robustness Against Bit Errors in Quantized DNN Weights
- Weight Clipping for Improved Bit Error Robustness
- Random Bit Error Training in PyTorch
The examples correspond to an article series on my blog: davidstutz.de.
The code is partly based on utilities from the examples in davidstutz/pytorch-adversarial-examples-training-articles.
Large parts of this repository are taken from my latest MLSys and TPAMI papers:
[1] D. Stutz, N. Chandramoorthy, M. Hein, B. Schiele.
Bit Error Robustness for Energy-Efficient DNN Accelerators.
MLSys, 2021.
[2] D. Stutz, N. Chandramoorthy, M. Hein, B. Schiele.
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators.
TPAMI, 2022
Installation is easy with Conda:
conda env create -f environment.yml
You can use python3 setup.py to check some of the requirements.
See environment.yml for details and versions.
Copyright (c) 2022 David Stutz, Max-Planck-Gesellschaft
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