Reconfigurable and Programmable Energy-Efficient Physical Computing

Speaker: Prof. Jennifer Hasler
Affiliation: Georgia Institute of Technology

Abstract: Physical computing systems, including analog, quantum, optical, and neuromorphic computing paradigms, show significant opportunities for energy efficient computation.  Digital computation is enabled by a framework developed over the last 80 years. Having an analog framework enables wider capability while giving the designer tools to make reasonable choices.  In the past, discussions on the capability of analog or physical computing were only of theoretical interest.  Analog computation becomes relevant with the advent of large-scale Field Programmable Analog Arrays (FPAA) devices.  Programmable and configurable large-scale analog circuits and systems enabling a typical factor of 1000 improvement in computational power (Energy) efficiency over their digital counterparts.  Scaling of energy efficiency, performance, and size will be discussed.  We will overview a few examples in this area as helps the resulting roadmap discussion, including speech, vision, and sensor interfaces.  These techniques are even more critical given the saturation of computational energy efficiency of digital multiply accumulate structures, the key component for high-performance computing.

Analog and digital systems have tools to model resolution and computational noise and computation energy; analog and digital approaches have their own optimal computing regions. We discuss the first step in analog (and mixed signal) abstraction utilized in FPAA  encoded in open-source SciLab / Xcos based toolset.  Abstraction of Blocks in the FPAA block library makes the SoC FPAA ecosystem accessible to system-level designers while still enabling circuit designers the freedom to build at a low level.

Biography: Jennifer Hasler is a Full Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology.  She received her Ph.D. from California Institute of Technology in Computation and Neural Systems in 1997 working with Carver Mead, and received her M.S. and B.S.E. in Electrical Engineering from Arizona State University in 1991. Her current research interests include low power electronics, mixed-signal system ICs, floating-gate MOS transistors, adaptive information processing systems, “smart” interfaces for sensors, cooperative analog-digital signal processing, device physics related to submicron devices or floating-gate devices, and analog VLSI models of on-chip learning and sensory processing in neurobiology.  Dr. Hasler received the NSF CAREER Award in 2001, and the ONR YIP award in 2002.  Dr. Hasler received the Paul Raphorst Best Paper Award, IEEE Electron Devices Society, 1997, IEEE CICC best paper award, 2005, Best student paper award, IEEE Ultrasound Symposium, 2006, IEEE ISCAS Sensors best paper award, 2005, and best demonstration paper, ISCAS 2010.   Jennifer Hasler has been involved in multiple startup companies launched out of Georgia Tech, as well as has been an author on over 350 technical journal and refereed conference papers and over 25 patents.

For more information, contact Prof. Ankur Mehta (mehtank@ucla.edu)

Date/Time:
Date(s) - Oct 08, 2018
12:30 pm - 1:30 pm

Location:
EE-IV Shannon Room #54-134
420 Westwood Plaza - 5th Flr., Los Angeles CA 90095