Women in Computing

Accelerating Image Processing Algorithms on Low-Power Embedded Systems

R. Iris Bahar

Professor of Engineering
Brown University
Friday, February 10, 2017
10:30am - 11:30am
3941 BBB

Add to Google Calendar

About the Event

Image features are broadly used in many embedded computer vision applications, from object detection and tracking to motion estimation and 3D reconstruction. Efficient feature extraction and description are crucial due to the real-time requirements of such applications over a constant stream of input data. High-speed computation typically comes at the cost of high power dissipation, yet embedded systems are often highly power constrained, making discovery of power-aware solutions especially critical for these systems. In this talk, I will present a power and performance evaluation of three low cost feature detection and description algorithms implemented on various embedded systems (embedded CPUs, GPUs and FPGAs). Our results demonstrate that, despite the high-level parallelization GPUs provide, being able to customize FPGAs to better handle the unique computation needs and memory access patterns of the algorithms leads to attractive solutions in terms of both performance and power.


R. Iris Bahar is currently a Professor of Engineering at Brown University. She has been on Brown’s faculty since 1996, in the School of Engineering, following completion of her PhD from University of Colorado, Boulder. Her research interests include computer architecture; computer-aided design for logic synthesis, verification, and low-power applications; and design, test, and reliability analysis for nanoscale systems. Her research has been continuously funded since 1997 through various industrial and government sources, including the National Science Foundation, DARPA, DoD, the Semiconductor Research Corporation (SRC), Intel, and IBM.

Additional Information

Contact: Dana Mickle

Phone: 734-764-9702

Email: dlmickle@umich.edu

Sponsor(s): Women in Computing Lecture Series

Faculty Sponsor: Chad Jenkins

Open to: Public