AD <sup>2</sup> VNCS: <u>A</u> dversarial <u>D</u> efense and <u>D</u> evice <u>V</u> ariation-Tolerance in Memristive Crossbar-Based <u>N</u> euromorphic <u>C</u> omputing <u>S</u> ystems
نویسندگان
چکیده
In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have obtained extremely high performance in neural network acceleration. However, adversarial attacks and conductance variations of memristors bring reliability challenges to NCS design. First, can fool the pose a serious threat security critical applications. On other hand, device lead degradation accuracy. this paper, we propose DFS (Deep Feature importance Sampling) BFS (Bayesian training strategies, which consist Bayesian Neural Network (BNN) prior setting, clustering-based loss function feature sampling techniques, simultaneously combat variation, white-box attack black-box challenges. Experimental results clearly demonstrate that proposed framework improve reliability.
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ژورنال
عنوان ژورنال: ACM Transactions on Design Automation of Electronic Systems
سال: 2023
ISSN: ['1084-4309', '1557-7309']
DOI: https://doi.org/10.1145/3600231