The scientific aim of this work is to study how small electronic devices can function at a high level in the face of increasingly-severe physical complexities. The complexities can originate from the physical signals of interest in a sensing system or from non-ideal device and algorithmic behaviors within the electronics itself, which are becoming unavoidable due to technology and system scaling. As an example, this study focuses on analyzing physiological signals that are available through low-power medical sensors. Though such signals are highly indicative, extracting medical information of value requires high-order models of the underlying physiological processes when in fact no tractable analytical models generally exist. This study also focuses on errors within the hardware that occur due to unpredictable but inevitable technological defects and variations, leading to high levels of errors in the data being processed. These challenges are approached through algorithmic methods emerging from the domain of machine learning that construct models for interpreting data from the data itself. The large amount of data that is available through small-scale sensors can thus be leveraged as an extensive knowledgebase; but the problem is that these methods are not well supported by low-power electronics, in terms of their computational energy, memory requirements, network interactions, etc. This research starts with the kernel computations used in machine-learning frameworks, and it investigates kernel formulations, structured hardware architectures, and algorithms to overcome the physical complexities associated with application signals and technological non-idealities. The principles are studied through hardware and software experimental demonstrations.

The broader impact of this research is to enable greater value of electronic systems in critical applications and to establish an interdisciplinary educational program that teaches students to connect fundamentals from computer science, low-power electronics, and clinical applications. While electronics presents tremendous capabilities, its impact on real-world challenges such as in healthcare depends on high-value interactions with physical systems. This program emphasizes clinical applications and collaborations to understand the role that electronics can play in enabling preemptive medical harm detection and chronic-disease management over large patient populations: something that is infeasible with today's methods. This program also emphasizes interactions with the semiconductor industry, to transfer principles and architectures both for advanced sensing platforms and for algorithmic approaches to hardware resilience; with hardware errors having been identified by the industry as one of the critical challenges, methods that overcome the need for traditional forms of design margining are being urgently pursued. New interdisciplinary courses, student projects, and outreach activities will expose students to external collaborators and will drive an educational program that ties together engineering fundamentals from multiple domains through an application-driven pursuit of systems to overcome critical challenges in healthcare decision support.

National Science Foundation (NSF)
Division of Computer and Communication Foundations (CCF)
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Sankar Basu
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Princeton University
United States
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