Saeid Nooshabadi (ECE/ICC) is the principal investigator on a project that has received $349,988 from the National Science Foundation for the project, “Collaborative Research: ACI-CDS&E: Highly Parallel Algorithms and Architectures for Convex Optimization for Realtime Embedded Systems (CORES).” This is a three-year project.
By Sponsored Programs.
Abstract
Embedded processors are ubiquitous, from toasters and microwave ovens, to automobiles, planes, drones and robots and are typically very small processors that are compute and memory constrained. Real-time embedded systems have the additional requirement of completing tasks within a certain time period to accurately and safely control appliances and devices like automobiles, planes, robots, etc. Convex optimization has emerged as an important mathematical tool for automatic control and robotics and other areas of science and engineering disciplines including machine learning and statistical information processing. In many fields, convex optimization is used by the human designers as optimization tool where it is nearly always constrained to problems solved in a few hours, minutes or seconds. Highly Parallel Algorithms and Architectures for Convex Optimization for Realtime Embedded Systems (CORES) project takes advantage of the recent advances in embedded hardware and optimization techniques to explore opportunities for real-time convex optimization on the low-cost embedded systems in these disciplines in milli- and micro-seconds.