Programmable and Configurable Analog Signal Processing

Paul Hasler, Ph.D.

Associate Professor, Georgia Institute of Technology

Abstract:

Recent work has shown the potential of using Programmable Analog Signal processing techniques for impacting low-power portable applications like imaging, audio processing, and speech recognition. The range of analog signal processing functions available results in many potential opportunities to incorporate these analog signal processing systems with digital signal processing systems for improved overall system performance. Programmable, dense analog techniques enable these approaches, based upon programmable transistor approaches. We show experimental evidence for the factor of 1000 to 10,000 power efficiency improvement for programmable analog signal processing compared to custom digital implementations.

We present a viewpoint showing that analog signal processing approaches are becoming configurable and programmable like their digital counterparts, while retaining a huge computational efficiency, for a given power budget, compared to their digital counterparts. We present recent results in programmable and configurable analog signal processing describing the widespread potential of these approaches. We also discuss issues with configurable systems, including size, power, and computational tradeoffs, as well as address the computational efficiency of these approaches.

Biography:

Paul Hasler is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. Dr. Hasler received his M.S. and B.S.E. in Electrical Engineering from Arizona State University in 1991, and received his Ph.D. from California Institute of Technology in Computation and Neural Systems in 1997. His 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, a Best paper award at SCI 2001, Best Sensor Track Paper, IEEE International Symposium on Circuits and Systems, Best Student Paper award, IEEE Custom Integrated Circuits Conference 2006, and Best Student Paper award, IEEE Ultrasound Symposium, 2006. Dr. Hasler is a Senior Member of the IEEE.

Thursday, June 7th at 3 p.m.