Energy-efficient scheduling on multi-FPGA reconfigurable systems

  • Authors:
  • Chao Jing;Yanmin Zhu;Minglu Li

  • Affiliations:
  • Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China and Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, Shangha ...;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China and Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, Shangha ...

  • Venue:
  • Microprocessors & Microsystems
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

With the growing demand in high performance computing, reconfigurable computing systems built with Field Programmable Gate Array (FPGA) have become increasingly popular for its reconfigurability and adaptability to applications. Although such systems promise high processing performance, their energy efficiency has become a critical issue. This paper studies the crucial problem of energy-efficient scheduling for reconfigurable systems with multiple FPGAs. Several factors make the energy efficient scheduling particularly challenging, including spatial allocation constraint, reconfiguration overhead, limited reconfiguration ports, and deadline satisfaction. These unique characteristics make energy efficient scheduling in multi-FPGA reconfigurable systems particularly challenging and none of existing solutions can be directly applied. This paper takes on this challenge and proposes an energy-efficient scheduling algorithm called AEE based on ant colony optimization for multi-FPGA reconfigurable systems. A task placement scheme is devised which serves as the heuristic function that derives the minimum global makespan, which is important to the ant colony algorithm based proposed in the paper. The scheme takes into account reconfiguration overhead and places tasks for reducing the overall overhead. Then, based on AEE, an enhanced algorithm (eAEE) is devised to deal with the tasks with precedence and interdependencies. To evaluate the effectiveness of the two proposed algorithms, comprehensive trace-driven simulations have been conducted and compared with other state-of-art algorithms. Experimental results demonstrate that AEE can successfully complete tasks without violating deadline constraints and the energy dissipation is largely reduced, no more than 10.65% higher than the optimum when the problem scale is relatively small. Also, eAEE consumes energy 58.17% less than an improved simulated annealing algorithm (iSA) with a large problem scale.