Abstract
This paper presents a novel technique that uses meta- heuristics and machine learning to automate the optimization of design parameters for reconfigurable designs. Traditionally, such an optimization involves manual application analysis as well as model and parameter space exploration tool creation. We develop a Machine Learning Optimizer (MLO) to automate this process. From a number of benchmark executions, we automatically derive the characteristics of the parameter space and create a surrogate fitness function through regression and classification. Based on this surrogate model, design parameters are optimized with meta-heuristics. We evaluate our approach using two case studies, showing that the number of benchmark evaluations can be reduced by up to 85% compared to previously performed manual optimization.
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Kurek, M., Becker, T., Luk, W. (2013). Parametric Optimization of Reconfigurable Designs Using Machine Learning. In: Brisk, P., de Figueiredo Coutinho, J.G., Diniz, P.C. (eds) Reconfigurable Computing: Architectures, Tools and Applications. ARC 2013. Lecture Notes in Computer Science, vol 7806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36812-7_13
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DOI: https://doi.org/10.1007/978-3-642-36812-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36811-0
Online ISBN: 978-3-642-36812-7
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