Combining Software and Hardware LCS for Lightweight On-chip Learning

Bernauer, Andreas; Zeppenfeld, Johannes; Bringmann, Oliver; Herkersdorf, Andreas

doi:10.1007/978-3-0348-0130-0_16

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The resulting classifiers are then translated into a form suitable for the hardware version of the XCS so that, at run time, learning continues, realizing self-adaptation at chip level. Learning at design time allows to use a less capable but lightweight hardware version, which could not learn a workable solution on its own [3].…”

Section: Methodsmentioning

confidence: 99%

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Autonomous multi-processor-SoC optimization with distributed learning classifier systems XCS

Bernauer

Arndt

Bringmann

2011

Proceedings of the 8th ACM International Conference on Autonomic Computing

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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.

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Section: Methodsmentioning

confidence: 99%

“…Each core is optimized by a separate LCS, which exchange classifiers with other LCS. The presented methodology extends our previous work [2,3], which did not consider the interaction of the cores of an MPSoC, e.g. reciprocal heating, and did not use distributed learning classifier systems.…”

Section: Introductionmentioning

confidence: 90%

Autonomous multi-processor-SoC optimization with distributed learning classifier systems XCS

Bernauer

Arndt

Bringmann

2011

Proceedings of the 8th ACM International Conference on Autonomic Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since event grouping and limited event abstraction is possible, the resulting system can be considered rudimentarily self-aware. In a similar spirit, Learning Classifier Sys-tems (LCS) [41] and eXtended Classifier Systems (XCS) [11] have been used to assess a systems state as a base for decision making such as load management and task allocation [6]. The decisions are coded in rules.…”

Section: Organic Computingmentioning

confidence: 99%