Abstract
Hardware/Software (HW/SW) partitioning is becoming an increasingly crucial step in embedded system co-design. To cope with roughly assumed parameters in system specification and imprecise benchmarks for judging the solution’s quality, researchers have been trying to find methods for a semi-optimal partitioning scheme in HW/SW partitioning for years. This paper proposes an application of a hybrid neural fuzzy system incorporating Boltzmann machine to the HW/SW partitioning problem. The hybrid neural fuzzy system’s architecture and performance estimation against simulated annealing algorithm are evaluated. The simulation result shows the proposed system outperforms other algorithm in cost and performance.
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Huang, Y., Kim, Y. (2007). Applying Hybrid Neural Fuzzy System to Embedded System Hardware/Software Partitioning. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_70
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DOI: https://doi.org/10.1007/978-3-540-74205-0_70
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