Hardware Security for Approximate Computing

近似计算的硬件安全

基本信息

批准号：
EP/X009602/1
负责人：
CHONGYAN GU
金额：
$ 37.98万
依托单位：
Queen's University Belfast
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FX009602%2F1
关键词：
Hardware Security Approximate Computing

项目摘要

As IBM's 2nm chip is pushing Moore's law approaching its limit, conventional computing techniques are struggling to offer high performance computing within power consumption constraints. Inspired by the fault tolerance capability of the human brain, approximate computing, which is error tolerant, can offer a huge reduction in computer power consumption without affecting the results (such as accuracy) of certain human perception and recognition related computation that only require a result to be approximate, rather than accurate. Examples include Artificial Intelligence (AI), Deep Learning (DL), image processing and even some cryptographic schemes. However, approximate computing has been shown to have security vulnerabilities due to the unpredictability of intrinsic errors that may be indistinguishable from malicious modifications. Due to the inherent power and area savings achieved by approximate computing, security countermeasures shold also be lightweight ande efficient. Hence, the aim of this proposal is to use advanced hardware security techniques to enable the development of approximate computing technologies that have both optimal security protection and optimal system efficiency. Currently, no comprehensive research has been conducted to date into security of approximate computing or into countermeasures that protect such designs.Physical unclonable function (PUF), as a lightweight hardware security primitive, is one of the best candidates for securing resource-constrained applications, such as approximate computing. A PUF can be used to generate a unique digital fingerprint for an electronic device based on manufacturing process variations of silicon chips. Currently, PUFs have been widely studied for conventional computing but no effective intrinsic PUF designs using approximate techniques have been presented. This project is timely because approximate computing has rapidly attracted attention from both academica and industry, as it addresses one of the fundamental barriers in computing systems, power dissipation, but it has also opened new vectors of attacks. This project will develop an intrinsic PUF design based on the normal operations of an approximate processor without the need for addtional hardware resource. The project will aslo address for the first time how to achieve secure and effective approximate computing designs.Thales UK, a leader in designing and building mission-critical information systems for the defence, security, aerospace, and transportation sections, has already invited the PI to join the Thales CyRes-Advance project to investigate security protection for connected and autonomous vechicles (CAVs) by considering hardware security. Thales will provide £250k in-kind support, such as technical advice/review of the hardware design, access to Thales CAV test platform and experimental validation for the project, to accelerate the research process and produce high-quality research outputs.

随着IBM的2NM芯片正在推动摩尔定律接近其极限，传统的计算技术正在努力在功耗限制内提供高性能计算。受人脑的可容忍能力的启发，近似计算的差异可容忍，可以大大降低计算机功耗，而不会影响某些人类认知和识别相关的计算结果（例如准确性），而这些计算仅需要结果即可是近似值，而不是准确的。例子包括人工智能（AI），深度学习（DL），图像处理甚至一些加密方案。但是，由于固有错误的不可预测性可能与恶意修改无法区分，因此已显示近似计算具有安全性漏洞。由于通过近似计算获得的继承功率和区域节省，安全对策也可以轻巧有效。因此，该提案的目的是使用先进的硬件安全技术来开发具有最佳安全保护和最佳系统效率的近似计算技术。当前，迄今为止，尚未进行近似计算的安全性或保护此类设计的对策的安全性。物理上的不元件功能（PUF）是一种轻量级硬件安全性，是确保资源约束应用程序（例如近似计算）的最佳候选者之一。 PUF可用于基于硅芯片的制造工艺变化，为电子设备生成独特的数字指纹。当前，PUF已被广泛研究用于常规计算，但没有提出使用近似技术的有效固有PUF设计。该项目之所以及时，是因为近似计算已经迅速吸引了学术和行业的关注，因为它涉及计算系统中的基本障碍之一，功率耗散，但它也打开了新的攻击媒介。该项目将基于近似处理器的正常操作开发固有的PUF设计，而无需增加附加硬件资源。该项目将首次解决如何实现安全有效的近似计算设计。 Thales将提供25万英镑的实物支持，例如硬件设计的技术建议/审查，访问Thales Cav测试平台以及对项目的实验验证，以加速研究过程并产生高质量的研究成果。