Neurocomputing for Minimizing Energy Consumption of Real-Time Operating System in the System-on-a-Chip

The RTOS (Real-Time Operating System) is a critical component in the SoC (System-on-a-Chip), which consumes the dominant part of total system energy. A RTOS system-level power optimization approach based on hardwaresoftware partitioning (RTOS-Power partitioning) can significantly minimize the energy consumption of a SoC. This paper presents a new model for RTOSPower partitioning, which helps in understanding the essence of the RTOSPower partitioning techniques. A discrete Hopfield neural network approach for implementing the RTOS-Power partitioning is proposed, where a novel energy function, operating equation and coefficients of the neural network are redefined. Simulations are carried out with comparison to other optimization techniques. Experimental results demonstrate that the proposed method can achieve higher energy savings up to 60% at relatively low costs.