CiMSAT: Exploiting SAT Analysis to Attack Compute-in-Memory Architecture Defenses
Jianfeng Wang, Huazhong Yang, Shuwen Deng, Xueqing Li
Abstract
Compute-in-memory (CiM) architecture is an emerging energy-efficient processing paradigm that has attracted widespread attention in AI and Internet of Things (IoT) applications. To protect statically stored sensitive data in CiM, designers have implemented various hardware obfuscation techniques in CiM architectures. However, we observe that existing CiM obfuscation defense strategies are based on straightforward static-key deployment strategies, which pose vulnerabilities from the perspective of key-pruning algorithms for de-obfuscation.
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