Title
Autonomous multi-processor-SoC optimization with distributed learning classifier systems XCS
Abstract
We present a methodology for autonomous optimization of multi-processor system-on-chips (MPSoC) using distributed learning classifier systems (LCS). The methodology promises to offer a high design reuse rate and to reduce design complexity. The distributed LCS autonomously optimize each core of an MPSoC and, by exchanging classifiers, help each other in achieving their individual goals, compensating reciprocal heating of the cores. We validate our methodology with a realistic simulation of an MPSoC inspired by the Cell processor, where the LCSs set the frequency and voltage of the cores to maximize each core's performance while minimizing temperature-dependent timing errors. In our example application, selecting emigrants by fitness, communicating along a complete graph, and deleting by prediction error results in the highest performance for each core that allows operation with almost no temperature-dependent timing errors but retains self-adaptation capabilities. The results show how distributed LCS autonomously optimize the cores of an MPSoC, greatly reducing design efforts through a reusable self-adaptation component and an according design methodology.
Year
DOI
Venue
2011
10.1145/1998582.1998632
ICAC
Keywords
Field
DocType
lcs autonomously optimize,autonomous multi-processor-soc optimization,cell processor,self-adaptation capability,design complexity,reusable self-adaptation component,temperature-dependent timing error,highest performance,design methodology,classifier system,high design reuse rate,design effort,autonomic computing,complete graph,prediction error,soc
Complete graph,Autonomic computing,Computer science,Reuse,Distributed learning,Real-time computing,Design methods,Organic computing,Classifier (linguistics),MPSoC,Distributed computing
Conference
Citations 
PageRank 
References 
5
0.48
18
Authors
4
Name
Order
Citations
PageRank
Andreas Bernauer1384.68
Gunnar Arndt250.48
Oliver Bringmann358671.36
Wolfgang Rosenstiel41462212.32