Robust Hyperdimensional Computing against Cyber Attacks and Hardware Errors: A Survey
Robust Hyperdimensional Computing against Cyber Attacks and Hardware Errors: A Survey
复制标题
针对网络攻击和硬件错误的鲁棒超维计算：一项调查
DOI：
10.1145/3566097.3568355
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Jiao, Xun
中科院分区：
文献类型：
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作者：
Ma, Dongning;Zhang, Sizhe;Jiao, Xun
超维计算（HDC），也称为向量符号架构（VSA），是一种新兴的人工智能算法，灵感来自人类大脑的功能方式。与深度神经网络（deep neural networks, dnn）相比，HDC具有模型尺寸更小、计算成本更低、一次/几次学习等优点，是一种很有前途的替代计算范式。随着人工智能在医疗保健和机器人等安全关键系统中的部署越来越多，不仅要力求高精度，还要确保其在高度不确定和敌对环境下的鲁棒性。然而，最近的研究表明，HDC就像dnn一样，容易受到网络攻击（例如，对抗性攻击）和硬件错误（例如，内存故障）。虽然越来越多的研究一直在研究HDC的稳健性，但对这一日益重要的主题的研究工作缺乏系统的回顾。据我们所知，本文提出了第一项调查，致力于回顾HDC对网络攻击和硬件错误的稳健性的研究工作。虽然HDC作为一种人工智能方法的性能和精度仍有待于未来的理论发展，但本文旨在为HDC的鲁棒性研究提供启示并呼吁社区努力。
Hyperdimensional Computing (HDC), also known as Vector Symbolic Architecture (VSA), is an emerging AI algorithm inspired by the way the human brain functions. Compared with deep neural networks (DNNs), HDC possesses several advantages such as smaller model size, less computation cost, and one/few-shot learning, making it a promising alternative computing paradigm. With the increasing deployment of AI in safety-critical systems such as healthcare and robotics, it is not only important to strive for high accuracy, but also to ensure its robustness under even highly uncertain and adversarial environments. However, recent studies show that HDC, just like DNNs, is vulnerable to both cyber attacks (e.g., adversarial attacks) and hardware errors (e.g., memory failures). While a growing body of research has been studying the robustness of HDC, there is a lack of systematic review of research efforts on this increasingly-important topic. To the best of our knowledge, this paper presents the first survey dedicated to review the research efforts made to the robustness of HDC against cyber attacks and hardware errors. While the performance and accuracy of HDC as an AI method still expects future theoretical advancement, this survey paper aims to shed light and call for community efforts on robustness research of HDC.