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Enabling and Exploiting Machine Learning in Ultra-low-power Devices

12:00 PM EST
Webinar - Online
Abira Sengupta –

The video and slides for this webinar can be downloaded on the SSCS Resource Center

Professional Development Hours (PDH's) can be requested for this webinar. Click here to request.

Abstract: Increasingly, sensing devices are required that are not only capable of acquiring complex embedded signals, but also capable of performing high-value analyses on the signals they acquire. Machine-learning algorithms play an important role, because they enable modeling and inference over signals that may otherwise be too complex to model through analytical methods. This talk looks at such algorithms first from the perspective of enabling them within severely resource-constrained devices, and then from the perspective of exploiting them towards more resource-efficient implementations of systems. Through several systems based on custom IC prototypes, this talk explores many avenues that machine-learning algorithms give rise to for addressing system bottlenecks. The focus on resource-efficient implementations directs us to use the algorithms in new ways, then leading to unconventional circuit architectures.


naveenBio: 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.

Event Recording
View Enabling and Exploiting Machine Learning in Ultra-low-power Devices recording