In-Sensor Computing: Opportunities and Challenges

  • Authors:
    Naveen Verma (Princeton), Patrick Mercier (UC/San Diego), Ana Claudia Arias (UC/Berkeley), Jan Rabaey (UC/Berkeley), Upamanyu Madhow (UC/Santa Barbara)
    Publication ID:
    P090754
    Publication Type:
    e-Workshop
    Received Date:
    26-Apr-2017
    Last Edit Date:
    27-Apr-2017
    Research:
    2385.002 (Stanford University)
    2385.003 (University of California/Berkeley)
    Replay:
    98 minutes
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Abstract

Many sensor systems measure quantities that cannot be described by simple physical models, and thus analytical techniques can be useful to infer underlying information out of noisy signals. However, both data computation techniques and radio transmission of raw recorded data can be prohibitively energy-expensive for wearable, implantable, or small IoT-like devices. The purpose of this e-workshop is to explore techniques that do preliminary computations such as feature extraction closer to the sensor, either in the analog or digital domains, with and without precise and/or reliable computational elements, to ease radio communication power, computation power, or both. This workshop explores where to place processing steps by exploring multiple layers of design hierarchy: devices, circuits, and algorithms; all of these layers which must work together to yield significant device-level power reductions.

Past Events

  Event Summary
25 April 2017
STARnet
STARnet
In-Sensor Computing: Opportunities and Challenges
Tuesday, April 25, 2017, 10:30 a.m.–noon CT
Urbana, IL, United States

E-Workshop

4819 Emperor Blvd, Suite 300 Durham, NC 27703 Voice: (919) 941-9400 Fax: (919) 941-9450

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