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Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. (arXiv:2104.08323v2 [cs.LG] UPDATED)
June 9, 2022, 1:20 a.m. | David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele
cs.CR updates on arXiv.org arxiv.org
Deep neural network (DNN) accelerators received considerable attention in
recent years due to the potential to save energy compared to mainstream
hardware. Low-voltage operation of DNN accelerators allows to further reduce
energy consumption, however, causes bit-level failures in the memory storing
the quantized weights. Furthermore, DNN accelerators are vulnerable to
adversarial attacks on voltage controllers or individual bits. In this paper,
we show that a combination of robust fixed-point quantization, weight clipping,
as well as random bit error training (RandBET) …
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