In this MLSys’21 paper, we consider the robustness of deep neural networks (DNN) against bit errors in their quantized weights. This is relevant in the context of DNN accelerators, i.e., specialized hardware for DNN inference: In order to reduce energy consumption, the accelerator’s memory may be operated at very low voltages. However, this induces exponentially increasing rates of bit errors that directly affect the DNN weights, reducing accuracy significantly. We propose a robust fixed-point quantization scheme, weight clipping as regularization during training and random bit error training to improve bit error robustness. This article shares my talk recorded for MLSys’21.
In January, I had the opportunity to interact with many other robustness researchers from academia and industry at the Robust Artificial Intelligence Workshop. As part of the workshop, organized by Airbus AI Research and TNO (Netherlands applied research organization), I also prepared a presentation talking about two of my PhD projects: confidence-calibrated adversarial training (CCAT) and bit error robustness of neural networks to enable low-energy neural network accelerators. In this article, I want to share the presentation; all other talks from the workshop can be found here.
In October this year, I was invited to talk at IBM’s FOCA workshop about my latest research on bit error robustness of (quantized) DNN weights. Here, the goal is to develop DNN accelerators capable to operating at low-voltage. However, lowering voltage induces bit errors in the accelerators’ memory. While such bit errors can be avoided through hardware mechanisms, such approaches are usually costly in terms of energy and area. Thus, training DNNs robust to such bit errors would enable low-voltage operation, reducing energy consumption, without the need for hardware techniques. In this 5-minute talk, I give a short overview.
In our ICML’20 paper, confidence-calibrated adversarial training (CCAT) addresses two problems of “regular” adversarial training. First, robustness against adversarial examples unseen during training is improved and second, clean accuracy is increased. CCAT biases the model towards predicting low-confidence on adversarial examples such that adversarial examples can be rejected by confidence thresholding. This article shares my talk on CCAT as recorded for ICML’20.
Many papers and theses provide high-level overviews of the proposed methods. Nowadays, in computer vision, natural language processing or similar research areas strongly driven by deep learning, these illustrations commonly include architectures of the used (convolutional) neural network. In this article, I want to provide a collection of examples using LaTeX and TikZ to produce nice figures of (convolutional) neural networks. All the discussed examples can also be found on GitHub.