A novel discrete hopfield neural network approach for hardware-software partitioning of RTOS in the soc

Authors:
Bing Guo;Yan Shen;Yue Huang;Zhishu Li
Affiliations:
School of Computer Science & Engineering, SiChuan University, ChengDu, China;School of Mechatronics Engineering, University of Electronic Science and Technology of China, ChengDu, China;Software College, Kyungwon University, Songnam, Gyeonggi-Do, South Korea;School of Computer Science & Engineering, SiChuan University, ChengDu, China
Venue:
EUC'06 Proceedings of the 2006 international conference on Emerging Directions in Embedded and Ubiquitous Computing
Year:
2006

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Abstract

The hardware-software automated partitioning of a RTOS in the SoC (SoC-RTOS partitioning) is a crucial step in the hardware-software co-design of SoC. First, a new model for SoC-RTOS partitioning is introduced in this paper, which can help in understanding the essence of the SoC-RTOS partitioning. Second, a discrete Hopfield neural network approach for implementing the SoC-RTOS partitioning is proposed, where a novel energy function, operating equation and coefficients of the neural network are redefined. Third, simulations are carried out with comparisons to the genetic algorithm and ant algorithm in the performance and search time used. Experimental results demonstrate the feasibility and effectiveness of the proposed method.