Ultra-fast and efficient algorithm for energy optimization by gradient-based stochastic voltage and task scheduling

Abstract

This paper presents a new technique, called Adaptive Stochastic Gradient Voltage-and-Task Scheduling (ASG-VTS), for power optimization of multicore hard realtime systems. ASG-VTS combines stochastic and energy-gradient techniques to simultaneously solve the slack distribution and task reordering problem. It produces very efficient results with few mode transitions. Our experiments show that ASG-VTS reduces number of mode transitions by 4.8 times compared to traditional energy-gradient-based approaches. Also, our heuristic algorithm can quickly find a solution that is as good as the optimal for a real-life GSM encoder/decoder benchmark. The runtime of ASG-VTS is 150 times and 1034 times faster than energy-gradient based and optimal ILP algorithms, respectively. Since the runtime of ASG-VTS is very low, it is ideal for design space exploration in system-level design tools. We have also developed a web-based interface for ASG-VTS algorithm.