Power Side-Channel Analysis and Mitigation for Neural Network Accelerators based on Memristive Crossbars

The modern trend of exploring Artificial Intelligence (AI) in various industries, such as big data, edge computing, automobile, and medical applications, has increased tremendously. As functionalities grow, energy-efficient hardware for AI devices becomes crucial. To address that, Computation-in-Memory (CiM) using Non-Volatile Memories (NVMs) offers a promising solution. However, security is also an important concern in this computation paradigm. In this work, we analyze the vulnerability for power side-channel attacks on Multiply-Accumulate (MAC) operations implemented in CiM architecture based on emerging NVMs. Our results show that peripheral devices such as Analog-to-Digital Converters (ADCs) leak much more sensitive information than the crossbar itself because of its significant power consumption. Therefore, we propose a circuit-level countermeasure based on hiding for the ADCs of memristive CiM architecture to mitigate the power attacks. The efficiency of our proposed countermeasure is shown by both attacks and leakage assessment methodologies using a maximum of one million measurement traces.