Ultra Low Power Secure Processors for Emerging Applications at the Edge

适用于边缘新兴应用的超低功耗安全处理器

• 批准号: RGPIN-2020-04179
• 负责人: EnrightJerger, Natalie
• 金额: $ 5.54万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751584
• 关键词: Ultra; Low; Power; Secure; Processors

Processors underpin our ability to compute. Users need to access, manipulate and analyze data in greater quantities, more efficiently and with better security. Enhanced processor designs are a combination of architectural innovations and technology improvements. Moore's Law (which doubles the number of transistors per unit area every 2 years) and Dennard scaling (which maintains constant power density as transistors shrink), have driven the underlying transistor technology that has resulted in better processors over the last 50 years. Unfortunately, due to physical limitations, Dennard scaling has ceased to apply and Moore's Law will end soon. Given the growing limitations to the underlying device technology, the burden is on architects to deliver increased performance and greater energy efficiency. At the same time, security and privacy have become first order processor design concerns; headline-making vulnerabilities such as SPECTRE highlight the dramatic need for more secure architectures. The proposed research addresses these pressing needs through secure approximate computing innovations to design better processors for edge and internet of things (IoT) devices running applications such as machine learning that require security and privacy-preserving mechanisms. These are increasingly critical domains: machine learning is leading the next computer revolution, and IoT devices are projected to number more than 80 billion by 2025, with uses in many sectors (e.g., health, finance, advanced manufacturing, transportation and communication). The approximate computing paradigm stems from the observation that not all applications require precise computation to produce an acceptable result. Applications amenable to approximation share at least one of three characteristics: noisy input, statistical computations, or a toleration of imprecision. Both machine learning applications and IoT devices that operate on noisy sensor data fall into this paradigm. Architects can leverage softer correctness requirements to trade accuracy for increased performance and/or reduced energy consumption. The proposed research leverages approximate computing to develop two objectives: 1) innovative approximate computing techniques that specifically enhance security and privacy and 2) new architectures that provide security and privacy with ultra-low power and area overheads targeting energy-harvesting IoT devices. The PI will address Equity, diversity and inclusion (EDI) by emphasizing EDI principles in HQP training, through the PI's professional service supporting EDI, and through the fundamental underpinnings of the research itself. Analyzing data sets containing information about under-represented individuals can potentially reveal sensitive information. Noise needs to be added to the data to obscure an individual's contribution. Through the design of secure architectures to aid in differential privacy, EDI concerns are at the forefront of this research agenda.

处理器支撑着我们的计算能力。用户需要更大量、更高效、更安全地访问、操作和分析数据。增强型处理器设计是架构创新和技术改进的结合。摩尔定律（每两年每单位面积的晶体管数量增加一倍）和登纳德缩放比例（随着晶体管缩小而保持恒定的功率密度）推动了基础晶体管技术的发展，在过去 50 年中，这种技术带来了更好的处理器。不幸的是，由于物理限制，登纳德缩放比例已不再适用，摩尔定律即将结束。鉴于底层设备技术的限制日益增加，架构师有责任提供更高的性能和更高的能源效率。与此同时，安全和隐私已成为处理器设计的首要考虑因素； SPECTRE 等成为头条新闻的漏洞突显了对更安全架构的迫切需求。拟议的研究通过安全近似计算创新来解决这些紧迫的需求，为运行需要安全和隐私保护机制的机器学习等应用程序的边缘和物联网 (IoT) 设备设计更好的处理器。这些领域日益重要：机器学习正在引领下一次计算机革命，预计到 2025 年物联网设备数量将超过 800 亿，并应用于许多领域（例如健康、金融、先进制造、交通和通信）。近似计算范式源于这样的观察：并非所有应用程序都需要精确计算才能产生可​​接受的结果。适合近似的应用程序至少具有以下三个特征之一：噪声输入、统计计算或不精确的容忍度。机器学习应用程序和基于噪声传感器数据运行的物联网设备都属于这种范例。架构师可以利用较软的正确性要求来牺牲准确性以提高性能和/或降低能耗。拟议的研究利用近似计算来开发两个目标：1）专门增强安全性和隐私性的创新近似计算技术，2）针对能量收集物联网设备以超低功耗和面积开销提供安全性和隐私性的新架构。 PI 将通过在 HQP 培训中强调 EDI 原则、通过 PI 支持 EDI 的专业服务以及通过研究本身的基本基础来解决公平、多样性和包容性 (EDI) 问题。分析包含代表性不足的个人信息的数据集可能会泄露敏感信息。需要在数据中添加噪音以掩盖个人的贡献。通过设计安全架构来帮助实现差异化隐私，EDI 问题成为本研究议程的首要问题。