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-TaskScheduling (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.

Chemical Reaction Optimization for Task Mapping in Heterogeneous Embedded Multiprocessor Systems

“…EE-GMA algorithm uses the traditional genetic algorithm. In the task scheduling and voltage scaling stage, we implemented the ASG-VTS [2,4] algorithm . ASG-VTS has a short running time and good energy saving effect, it can take advantage of the energy-saving potential of the task mapping algorithm.…”

“…There are many papers [2,4] that focus solely on the TSVS subproblem and there are also some papers [3,9] that assume that the task ordering is known and focus only on voltage scaling.…”