Work

Attack and Defense: Enhancing Robustness of Binary Hyper-Dimensional Computing

Year: 2025

Type: article

Abstract: Hyper-Dimensional Computing (HDC) has emerged as a lightweight computational model, renowned for its robust and efficient learning capabilities, particularly suitable for resource-constrained hardware. As HDC often finds its application in edge devices, the associated security challenges pose a critical concern that cannot be ignored. In this work, we aim to quantitatively delve into the robustness of binary HDC, which is widely recognized for its robustness. Employing the bit-flip attack as our initial focal point, we meticulously devise both an attack mechanism and a corresponding defense mechanism. Our objective is to comprehensively explore the robustness of the binary hyper-dimensional computation model, aiming to gain a deeper understanding of its security vulnerabilities and potential defenses. Specifically, we introduce a novel attack framework for HDC, named HyperAttack, which is capable of compromising a robust binary HDC model by maliciously flipping a minimal number of bits within its memory system (specifically, the DRAM) that houses the associative memory. The bit-flip operation is executed through the well-known Row Hammer attack, and HyperAttack optimizes the accuracy degradation by pinpointing the most vulnerable bits in the hyper-dimensional vectors (represented as binary vectors within the associative memory) of the HDC model. The proposed HyperAttack framework is grounded in the principles of fuzziness, seamlessly integrating dimensional ranking and feature similarity analysis within hypervectors to precisely identify the bits to be flipped. Furthermore, we have developed a defense mechanism named HyperDefense, designed to bolster the robustness of binary hyper-dimensional computational models against bit-flip attacks. This defense scheme is tailored specifically for HDC models, providing a robust safeguard against potential threats. HyperDefense operates directly on the associative memory of HDC models, strengthening their defenses. By meticulously modifying selected bits, HyperDefense maintains a high level of accuracy close to the original model, even in the face of increased bit flip rates. This defense mechanism leverages redundant dimensions as backups for critical information. Through a thorough analysis of dimension importance, HyperDefense achieves superior robustness by gracefully sacrificing non-critical dimensions, thus ensuring the model’s robustness against potential attacks.

Source: ACM Transactions on Architecture and Code Optimization

DOI: 10.1145/3736172