Exploiting Data-driven Inference Towards Low-energy Implementations in Intelligent Sensors

Tuesday, March 21, 2017
11:00 AM to 12:00 PM
POB 2.402
Free and open to the public

For designers of sensor systems, faced with increasingly severe resource constraints (energy, area, bandwidth reliability), the focus on inferences from sensor data, rather than the sensor data itself, is a VERY liberating thing. While sensor data may express inferences of interest through extremely complex correlations, we now know quite broadly that these can be effectively modeled and analyzed through data-driven algorithms. What is liberating is that research in low-power systems is showing that not only can such algorithms be effectively mapped to resource-constrained implementations, but in fact such algorithms can actually relax the implementations themselves. As an example, I describe how data-driven learning enables us to select inference functions and/or parameters that are preferred from the perspective of low-energy implementation and further enables the implementations to exhibit substantially imperfect behaviors. Then, I look at how this can be exploited within systems architectures to alleviate traditional pain points (sensor acquisition, data conversion, memory operations). Measured results from several custom integrated-circuit prototypes are presented.

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Naveen Verma

Princeton University

Naveen Verma received the B.A.Sc. degree in Electrical and Computer Engineering from the University of British Columbia, Vancouver, Canada in 2003, and the M.S. and Ph.D. degrees in Electrical Engineering from Massachusetts Institute of Technology in 2005 and 2009 respectively. Since July 2009 he has been with the department of Electrical Engineering at Princeton University, where he is currently an Associate Professor. His research focuses on advanced sensing systems, including low-voltage digital logic and SRAMs, low-noise analog instrumentation and data-conversion, large-area sensing systems based on flexible electronics, and low-energy algorithms for embedded inference, especially for medical applications. Prof. Verma is a Distinguished Lecturer of the IEEE Solid-State Circuits Society, and serves on the technical program committees for ISSCC, VLSI Symp., DATE, and IEEE Signal-Processing Society (DISPS). Prof. Verma is recipient or co-recipient of the 2006 DAC/ISSCC Student Design Contest Award, 2008 ISSCC Jack Kilby Paper Award, 2012 Alfred Rheinstein Junior Faculty Award, 2013 NSF CAREER Award, 2013 Intel Early Career Award, 2013 Walter C. Johnson Prize for Teaching Excellence, 2013 VLSI Symp. Best Student Paper Award, 2014 AFOSR Young Investigator Award, 2015 Princeton Engineering Council Excellence in Teaching Award, and 2015 IEEE Trans. CPMT Best Paper Award.