Energy-Efficient Task Allocation in Heterogeneous NoC-Based MPSoCs Using Binary Chimp Optimization

Resumen

Modern embedded applications keep getting more complex, while battery-powered platforms still face strict energy limits. This makes it crucial to design energy-efficient task mapping strategies for Network-on-Chip (NoC)-based heterogeneous Multi-Processor System-on-Chip (MPSoCs). To address this, we present a novel discrete optimization technique called the Binary Chimp Optimization Algorithm (BChOA). It is built to cut energy use when mapping multiple application tasks onto heterogeneous MPSoC architectures.

BChOA takes the population behavior from the original Chimp Optimization Algorithm (ChOA) and modifies it to work more effectively with binary problems. It employs a sigmoid-based method to handle the transition from continuous to discrete, enabling it to search through the space of possible task-to-core assignments more effectively. We tested the effectiveness of BChOA by comparing it to three popular optimization methods: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimisation (ACO). We ran all of them on standard application benchmarks like VOPD, MPEG-4, MMS, MWD, and PIP.

BChOA ultimately proved to be more energy-efficient across the board. On average, it used 7.73% less energy than GA, 4.50% less than PSO, and 1.71% less than ACO. We also examined where each of these methods excelled and where they fell short, all within the context of task mapping for NoC-based heterogeneous MPSoC systems.