Ultra Low Power Secure Processors for Emerging Applications at the Edge

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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.