In April, I was invited to talk about my work on random or adversarial bit error robustness of (quantized) deep neural networks in Katharina Morik’s group at TU Dortmund. The talk is motivated by DNN accelerators, specialized chips for DNN inference. In order to reduce energy-efficiency, DNNs are required to be robust to random bit errors occurring in the quantized weights. Moreover, RowHammer-like attacks require robustness against adversarial bit errors, as well. While a recording is not available, this article shares the slides used for the presentation.
The slides can be downloaded here and the corresponding papers are available on ArXiv:
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele. Bit Error Robustness for Energy-Efficient DNN Accelerators. MLSys, 2021.
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele. Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. ArXiv, 2021.