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.

处理器是我们计算能力的基础。用户需要更有效，更有效地访问，操纵和分析数据。增强的处理器设计是建筑创新和技术改进的结合。摩尔定律（每2年每2年每单位区域的晶体管数量翻了一番）和丹纳德缩放（随着晶体管的缩小，保持恒定的功率密度）驱动了基础晶体管技术，在过去的50年中，晶体管技术导致了更好的处理器。不幸的是，由于身体上的局限性，丹纳德·比尔辛（Dennard Scaling）停止了申请，摩尔法律将很快结束。鉴于对基础设备技术的局限性越来越大，负担负担在建筑师方面提供提高的性能和提高能源效率。同时，安全性和隐私已成为一阶处理器设计问题； Spectre等标题制造漏洞突出了对更安全的体系结构的巨大需求。拟议的研究通过安全的近似计算创新来满足这些压力需求，以设计更好的处理器和物联网（IoT）设备，例如运行需要安全性和隐私机制的机器学习等应用程序。这些是越来越关键的领域：机器学习正在领导下一次计算机革命，到2025年，IoT设备预计将超过800亿，在许多领域（例如，健康，财务，高级制造，运输和通信）的用途。近似计算范式源于这样的观察结果：并非所有应用都需要精确的计算以产生可接受的结果。适用于近似值的应用具有至少三个特征之一：嘈杂的输入，统计计算或不精确的耐受性。在嘈杂的传感器数据上运行的机器学习应用程序和IoT设备都属于此范式。建筑师可以利用较软性的要求来贸易准确性，以提高性能和/或降低能源消耗。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以及2）新的体系结构，这些架构提供了具有超低功率的安全性和隐私性，并以超低的功率和面积为目标，以能源收获的物联网设备。 PI将通过支持EDI的PI专业服务以及研究本身的基本基础来强调HQP培训中的EDI原则，以解决公平，多样性和包容性（EDI）。分析包含有关代表性不足个人的信息的数据集可以潜在地揭示敏感信息。需要将噪声添加到数据中，以掩盖个人的贡献。通过设计安全的体系结构以帮助差异隐私，EDI的关注是该研究议程的最前沿。