Energy-Efficient Compressed Sensing: A joint Algorithmic/Implementation Approach Using Deterministic Sensing

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The objective of this research is to develop low-power, robust, and energy-efficient sensors to satisfy a wide range of current applications including large-scale networks and implantable biomedical sensors. The aim is to develop the fundamental framework for sensor systems, connecting theory and algorithms with efficient hardware implementations and circuit metrics, such as power, footprint, quantization effects, and other circuit and channel non-idealities. The approach is to develop compressed sensing techniques that result in universal and efficient sensor designs. Intellectual Merit: The transformative aspect of this research is a joint investigation into both theoretical and hardware development of compressed sensing techniques for sensor systems. The plan is to optimize energy allocation in the information chain, by shifting from (the original) randomization techniques to more energy-efficient deterministic sensing techniques. The focus is on regimes relevant to practice, and the results of this research are foreseen to greatly impact both systems and hardware communities.Broader Impacts: Energy-efficient sensor nodes are in great demand today. For example in large-scale networks and implantable sensors, low power nodes are required to increase the average node life-time until maintenance, as well as improve the patient's quality of life. The goal is to convince practitioners, that compressed sensing can be mapped to hardware-efficient implementations. By creating an online portal, the project will also demonstrate the benefits of the developed sensing techniques to both experts and general population. To disseminate the fundamental systems/hardware framework, a new multi-disciplinary course will be created at MIT and offered to a wide-range of students.