Hardware-software partitioning of embedded operating system in the SoC using a discrete hopfield neural network approach

The embedded operating system is a key component in the SoC (System-on-a-chip) while hardware-software automated partitioning of embedded operating system in the SoC (SoC-EOS partitioning) is a crucial step in the hardware-software co-design of SoC. This paper presents a new model for SoC-EOS partitioning, which can improve in understanding the essence of SoC-EOS partitioning techniques. Then, a novel discrete Hopfield neural network approach for SoC-EOS partitioning is proposed, where the energy function, operating equation and coefficients of Hopfield neural network that is applicable to SoC-EOS partitioning are defined. Last, 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.