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VINTAN Lucian, CHIS R., MD. ALI ISMAIL, COTOFANA C. Improving Computing Systems Automatic Multi-Objective Optimization through Meta-Optimization. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ISSN: 0278-0070, DOI 10. 1109/TCAD. 2015. 2501299, 2015.

Abstract: This article presents the extension of FADSE tool using a meta-optimization approach, which is used to improve the performance of design space exploration algorithms, by driving two different multi-objective meta-heuristics concurrently. More precisely, we selected two genetic multi-objective algorithms: NSGA-II and SPEA2, that work together in order to improve both the solutions' quality and the convergence speed. With the proposed improvements, we ran FADSE in order to optimize the hardware parameters' values of the Grid ALU Processor (GAP) micro-architecture from a bi-objective point of view (performance and hardware complexity). Using our developed approach we obtained better GAP instances (a configuration has for almost the same CPI - 1.00, the hardware complexity 38% smaller/better - 35.81 vs 58.61) in half of the time compared to a classical sequential optimization approach (5 days vs. 10 days).

Keywords: Design Space Exploration, Multi-objective Optimization Algorithms, Meta-Optimization, Grid ALU Processor


Posted by Lucian (N.) Vintan


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