PGI-14-hosted Seminar - Prof. Jennifer Hasler, Georgia Institute of Technology
Dear Colleagues,
We cordially invite you to the PGI-14-hosted Seminar on 29th September 2022, 10:30 am.
Please feel free to forward to those in your team who might be interested.
Additionally, Prof. Hasler would be pleased to meet with interested parties during her time in Aachen. Any individuals or groups interested to meet on September 29th or 30th can send a brief note to: j.strachan@fz-juelich.de or p.wagner@fz-juelich.de. It may also be possible to arrange ad-hoc meetings following Prof. Hasler’s seminar.
The talk will take place as a hybrid event in:
Room WSH S1
RWTH Aachen University
Walter-Schottky-Haus,
1st floor/1. Obergeschoss
Sommerfeldstr. 18
52074 Aachen
and a video conference in parallel. To join the Zoom meeting:
https://rwth.zoom.us/j/97517018709
Meeting ID: 975 1701 8709
Prof. Dr. Jennifer Hasler
Georgia Institute of Technology, School of Electrical and Computer Engineering
will give a talk on:
Physical Computing & Building Large-Scale Brain Computations
Abstract: Cognitive Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are meeting hard physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Neuromorphic techniques are of increasing interest along with other Physical computing directions, such as Analog, Quantum, and Optical computation.
Understanding and developing computational theory of physical computation became relevant with the advent of large-scale Field Programmable Analog Arrays (FPAA) as well as other recent physical computing implementations. Digital computation is enabled by a framework developed over the last 80 years. Analog computing techniques result in 1000x improvement in power or energy efficiency, and a 100x improvement in area efficiency, compared to digital computation. FPAA structures have demonstrated applications examples range from acoustics and sensor processing, classification, embedded machine learning, image processing, communications, RF signal processing, and optimal path planning using coupled PDEs.
Towards this end, we provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore’s law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.
Bio: : 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.
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.