Side-channel Collision Attacks on Hyper-Dimensional Computing based on Emerging Resistive Memories

Brain-inspired architectures are increasingly favored for edge devices due to their efficient execution of cognitive tasks with limited energy and computational resources. A promising approach in this field is Hyper-Dimensional Computing (HDC), known for its robustness against noise and simple computational operations, despite being constrained by memory bandwidth. HDC is well-suited for computation in memory (CiM) using emerging resistive memory technologies. However, security concerns arise from potential attack vectors in HDC, spanning from computational algorithms to the underlying technology. Since HDC relies on unique data patterns, or class hypervectors, stored in memory, there is a risk of undetected data manipulation or poisoning. We demonstrate that power information from insensitive (public) outputs can expose secret data stored in memories. This study investigates side-channel vulnerabilities in Content Addressable Memory (CAM)-HDC implemented with resistive memory-based CiM. We develop a collision attack using side-channel information to recover predicted classes from all possible outputs accurately. Our findings highlight a security threat in HDC even with parallel computation between query and class hypervectors. ... mehrTo address this vulnerability, we propose an effective countermeasure based on a hiding technique for CiM implementation, mitigating the identified security risks.