17th INTERNATIONAL FORUM ON MPSoC
for software-defined hardware
For further information, please send email to Frédéric Pétrot
Kyushu University, Japan
Predictive Sensing and Adaptive Optimization for Real-Time Applications
This talk introduces two predictive sensing techniques for emerging applications. First, we focus on an on-line object tracking system that has been a core technology in computer vision and its importance has been increasing rapidly. Because this technology is utilized for battery-operated products, energy consumption must be minimized. To tackle with this challenge, a run-time frame-rate optimization technique is introduced. Second, this talk focuses on a novel manycore execution strategy for real-time model predictive controls. The key idea is to exploit predicted input (or sensed) values, which are produced by the model predictive control itself, to speculatively solve optimal control problems. It is well known that control applications are not suitable for manycore processors, because feedback-loop systems inherently stand on sequential operations. Since the proposed scheme does not rely on conventional thread-/data-level parallelism, it can be easily applied to such control systems.
Koji Inoue received the B.E. and M.E. degrees in computer science from Kyushu Institute of Technology, Japan in 1994 and 1996, respectively. He received the Ph.D. degree in Department of Computer Science and Communication Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan in 2001. In 1999, he joined Halo LSI Design& Technology, Inc., NY, as a circuit designer. He is currently a professor of the Department of I&E Visionaries, Kyushu University. His research interests power-aware computing, high-performance computing, dependable processor architecture, secure computer systems, 3D microprocessor architectures, and multi/many-core architectures.