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• W2334817123 abstract "This paper presents a new approach for mapping and scheduling task graphs for heterogeneous hardware/software computing systems using heuristic search. Task mapping and scheduling are vital in hardware/software codesign and previous approaches that treat them separately lead to suboptimal solutions. In this paper, we propose two techniques to enhance the speedup of mapping/scheduling solutions: (1) an integrated technique combining task clustering, mapping, and scheduling, and (2) a multiple neighborhood function strategy. Our approach is demonstrated by case studies involving 40 randomly generated task graphs, as well as six applications. Experimental results show that our proposed approach outperforms a separate approach in terms of speedup by up to 18.3% for a system with a microprocessor, a floating-point digital signal processor, and an FPGA." @default.
• W2334817123 created "2016-06-24" @default.
• W2334817123 creator A5066865687 @default.
• W2334817123 date "2012-02-29" @default.
• W2334817123 modified "2023-10-01" @default.
• W2334817123 title "Integrated Task Clustering, Mapping and Scheduling for Heterogeneous Computing Systems" @default.
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• W2334817123 doi "https://doi.org/10.5121/ijcsit.2012.4111" @default.
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