Ultra Low Power Secure Processors for Emerging Applications at the Edge

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(每两年使单位面积的晶体管数量翻一番)和Dennard比例(在晶体管缩小时保持恒定的功率密度)推动了潜在的晶体管技术，在过去50年里带来了更好的处理器。不幸的是，由于物理条件的限制，Dennard比例法已不再适用，摩尔定律也将很快终止。鉴于底层设备技术的局限性越来越大，架构师肩负着提供更高性能和更高能效的负担。与此同时，安全和隐私已经成为处理器设计的第一要务；像SPECTE这样的头条新闻漏洞突显了对更安全体系结构的巨大需求。 这项拟议的研究通过安全的近似计算创新来满足这些紧迫的需求，为运行机器学习等需要安全和隐私保护机制的应用程序的边缘和物联网(IoT)设备设计更好的处理器。这些都是日益重要的领域：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800亿台，应用于许多行业(例如，医疗、金融、先进制造、交通和通信)。 近似计算范例源于这样一种观察，即并不是所有的应用程序都需要精确的计算来产生可接受的结果。适用于近似的应用程序至少具有以下三个特征之一：噪声输入、统计计算或容忍不精确度。机器学习应用程序和处理噪声传感器数据的物联网设备都属于这种模式。架构师可以利用更软的正确性要求来换取更高的性能和/或更低的能耗。拟议的研究利用近似计算来开发两个目标：1)创新的近似计算技术，专门增强安全和隐私；2)以超低功耗和面积开销提供安全和隐私的新架构，目标是收集能量的物联网设备。 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。分析包含代表不足个人信息的数据集可能会泄露敏感信息。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来帮助区分隐私，EDI问题是这项研究议程的首要问题。

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.