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  • IEEE SSCS distinguished lecture:106/7/31(一)14:15 於台達館R216,Naveen Verma ( Princeton University ), Exploiting Data-driven Inference Towards Low-energy
  • 發佈單位: 支會原始連結
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  • Location: 清大台達館216室
  • Content:
    SSCS DL Speaker:Prof. Naveen Verma ( Princeton University )
    106年7月31日(星期一) 14:15 - 15:30

    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.

    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.

    主辦單位: IEEE SSCS Taipei Chapter
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  • Contact Person: Meng-Fan (Marvin) Chang
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