Abstract
Nowadays one of the most vital problems in embedded system co-design is Hardware/Software (HW/SW) partitioning. Due to roughly assumed parameters in design specification and imprecise benchmarks for judging the solution’s quality, embedded system designers have been working on finding a more efficient method for HW/SW partitioning for years. We propose an application of a hybrid neural fuzzy system incorporating Boltzmann machine to the HW/SW partitioning problem. Its architecture and performance estimation against other popular algorithm are evaluated. The simulation result shows the proposed system outperforms other algorithm both in cost and performance.
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Huang, Y., Kim, YS. (2007). Boltzmann Machine Incorporated Hybrid Neural Fuzzy System for Hardware/Software Partitioning in Embedded System Design. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_29
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DOI: https://doi.org/10.1007/978-3-540-73729-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73728-5
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