Paper Info
Title
On low power high level synthesis using genetic algorithms
Abstract
This paper describes a new approach for utilizing genetic algorithms to solve high-level synthesis tasks with multiple voltages. The novelty of the method lies in its approach for modeling and encoding the resulting chromosomes. The proposed system receives a control/data flow graph, hardware library, and the constraints for area, time, and power as inputs. It then uses this information to concurrently solve the scheduling, binding, and allocation problems in order to generate a solution optimized for average and peak power. The new formulation eliminates the need to check for a valid schedule since it always generates a valid one. This leads to a simple evaluation function and a quick solution. The evaluation function is a mix of constraints satisfaction and a consideration of average and peak power. Simulation results show that the lower power solutions are obtained in a reasonable CPU time, which is in the range of seconds.
Year
DOI
Venue
2002
10.1109/ICECS.2002.1046271
Electronics, Circuits and Systems, 2002. 9th International Conference
Keywords
Field
DocType
VLSI,application specific integrated circuits,circuit CAD,circuit optimisation,data flow graphs,genetic algorithms,high level synthesis,integrated circuit design,low-power electronics,processor scheduling,ASIC design,CPU time,VLSI design,allocation problems,area constraints,average power,binding problems,chromosome encoding,concurrent solution,constraints satisfaction,control/data flow graph,evaluation function,genetic algorithms,hardware library,low power high level synthesis,modeling,multiple voltage high-level synthesis,peak power,power constraints,scheduling problems,simulation,time constraints,valid schedule generation
CPU time,Scheduling (computing),Computer science,High-level synthesis,Data-flow analysis,Evaluation function,Electronic engineering,Very-large-scale integration,Genetic algorithm,Low-power electronics
Conference
Volume
ISBN
Citations 
2
0-7803-7596-3
4
PageRank 
References 
Authors
0.56
5
2
Name
Order
Citations
PageRank
Mohamed A. Elgamel1649.44
Magdy A. Bayoumi2803122.04