Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Configuration prefetch for single context reconfigurable coprocessors
FPGA '98 Proceedings of the 1998 ACM/SIGDA sixth international symposium on Field programmable gate arrays
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Computers
Configuration Caching Management Techniques for Reconfigurable Computing
FCCM '00 Proceedings of the 2000 IEEE Symposium on Field-Programmable Custom Computing Machines
A parallel configuration model for reducing the run-time reconfiguration overhead
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Circuits and architectures for field programmable gate array with configurable supply voltage
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Proceedings of the 2009 International Conference on Computer-Aided Design
Selective instruction set muting for energy-aware adaptive processors
Proceedings of the International Conference on Computer-Aided Design
GreenSLAs for the energy-efficient management of data centres
Proceedings of the 2nd International Conference on Energy-Efficient Computing and Networking
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In this paper, an approach that uses dynamic voltage scaling (DVS) to reduce the configuration energy of runtime reconfigurable devices is proposed. The basic idea is to use configuration prefetching and parallelism to create excessive system idle time and apply DVS on the configuration process when such idle time can be utilized. A genetic algorithm is developed to solve the task scheduling and voltage assignment problem. With real applications, the results show that up to 19.3% of configuration energy can be reduced. When considering the reduction of the configuration energy, the results show that using more computation resources is more favorable when the configuration latency is relatively small, and using more configuration controllers is more favorable for relatively large latency.