Hardware Security for Approximate Computing

随着IBM的2 NM芯片将摩尔定律推向极限，传统的计算技术正努力在功耗限制下提供高性能计算。受人脑容错能力的启发，容错近似计算可以在不影响某些与人类感知和识别相关的计算结果(如准确性)的情况下，大大降低计算机的功耗，这些计算只要求结果是近似的，而不是准确的。例如人工智能(AI)、深度学习(DL)、图像处理，甚至一些密码方案。然而，由于内部错误的不可预测性，可能无法与恶意修改区分，因此近似计算已被证明具有安全漏洞。由于通过近似计算实现的固有功率和面积节省，安全对策也应该是轻量级和高效的。因此，本方案的目的是使用先进的硬件安全技术，使近似计算技术的发展既具有最佳的安全保护，又具有最佳的系统效率。物理不可克隆函数(PUF)作为一种轻量级硬件安全原语，是解决资源受限应用(如近似计算)安全的最佳方案之一。PUF可用于基于硅芯片的制造工艺变化为电子设备生成唯一的数字指纹。目前，PUF已被广泛研究用于常规计算，但还没有提出有效的基于近似技术的本征PUF设计。这个项目是及时的，因为近似计算迅速吸引了学术界和工业界的注意，因为它解决了计算系统中的基本障碍之一--功耗，但它也开启了新的攻击载体。该项目将开发一种基于近似处理器的正常操作的固有PUF设计，而不需要额外的硬件资源。该项目还将首次解决如何实现安全和有效的近似计算设计。泰利斯英国公司，一家为国防、安全、航空航天和交通部门设计和构建关键任务信息系统的领先者，已经邀请PI加入泰利斯Cyres-Advance项目，通过考虑硬件安全来研究联网和自主车辆(CAV)的安全保护。泰利斯公司将提供GB 250k实物支持，如硬件设计的技术建议/审查、进入泰利斯CAV测试平台和项目的实验验证，以加快研究进程并产生高质量的研究成果。

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.