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公开(公告)号:US20220318628A1
公开(公告)日:2022-10-06
申请号:US17714677
申请日:2022-04-06
申请人: Sai Kiran Cherupally , Jian Meng , Shihui Yin , Deliang Fan , Jae?sun Seo
发明人: Sai Kiran Cherupally , Jian Meng , Shihui Yin , Deliang Fan , Jae?sun Seo
摘要: Hardware noise-aware training for improving accuracy of in-memory computing (IMC)-based deep neural network (DNN) hardware is provided. DNNs have been very successful in large-scale recognition tasks, but they exhibit large computation and memory requirements. To address the memory bottleneck of digital DNN hardware accelerators, IMC designs have been presented to perform analog DNN computations inside the memory. Recent IMC designs have demonstrated high energy-efficiency, but this is achieved by trading off the noise margin, which can degrade the DNN inference accuracy. The present disclosure proposes hardware noise-aware DNN training to largely improve the DNN inference accuracy of IMC hardware. During DNN training, embodiments perform noise injection at the partial sum level, which matches with the crossbar structure of IMC hardware, and the injected noise data is directly based on measurements of actual IMC prototype chips.
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2.
公开(公告)号:US20200349247A1
公开(公告)日:2020-11-05
申请号:US16864902
申请日:2020-05-01
申请人: Jae-sun Seo , Shihui Yin , Sai Kiran Cherupally
发明人: Jae-sun Seo , Shihui Yin , Sai Kiran Cherupally
摘要: A smart hardware security engine using biometric features and hardware-specific features is provided. The smart security engine can combine one or more entropy sources, including individually distinguishable biometric features, and hardware-specific features to perform secret key generation for user registration and authentication. Such hybrid signatures may be distinct from person-to-person (e.g., due to the biometric features) and from device-to-device (e.g., due to the hardware-specific features) while varying over time. Thus, embodiments described herein can be used for personal device authentication as well as secret random key generation, significantly reducing the scope of an attack.
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