GA-MMAS: an Energy- and Latency-aware Mapping Algorithm for 2D Network-on-Chip

摘要

In this paper, a new mapping algorithm named GA-MMAS is proposed based on Genetic Algorithm (GA) and MAX-MIN Ant System Algorithm (MMAS), using a unified cost function with energy and link load variance, to optimize energy consumption and latency for NoC. Firstly the proposed algorithm obtain the elicitation information via priority mapping of IP core with larger communication volume, instead of using heuristics, to improve the optimal solution of MMAS. Then with the combination of MMAS and GA, the advantage of speed in GA makes compensation to the lack of pheromone in the early stage of MMAS, and in turn enhancing the accuracy of optimal solution, which leads to lower energy consumption and latency. The experiments performed on various random benchmarks and a complex video/audio application to conform the efficiency of the algorithm. Experimental results show that when only optimizing energy consumption, the algorithm saves about 36%～60%, 3%~25%, 10%~30% and 3%~30% of energy consumption compared to random mapping, GA, Ant Colony Algorithm (ACA) and MMAS respectively. When only optimizing latency, the algorithm decreases about 36%～60%, 3%~25%, 10%~30% and 3%~30% of link load variances. When optimizing both of energy consumption and latency, the cost reducing results are 36%～60%, 3%～25%, 10%～30% and 3%～30%.