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Bit Error Robustness in PyTorch Article Series

I was planning to have an article series on bit error robustness in deep learning — similar to my article series on adversarial robustness — with accompanying PyTorch code. However, the recent progress in machine learning made me focus on other projects. Nevertheless, the articles should have been:

• 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

Large parts of this repository are taken from my latest MLSys'21 [1] and TPAMI'22 [2] 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.