ArXiv Pre-Print “Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators”

Deep neural network (DNN) accelerators are popular due to reduced cost and energy compared to GPUs. To further reduce energy consumption, the operating voltage of the on-chip memory can be reduced. However, this injects random bit errors, directly impacting the (quantized) DNN weights. As result, improving DNN robustness against these bit errors can significantly improve energy efficiency. Similarly, these chips are subject to bit-level hardware- or software-based attacks. In this case, robustness against adversarial bit errors is required to improve security of DNN accelerators. Our paper presented in this article addresses both problems.

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