基于动态自适应离散粒子群算法的3D NoC低功耗映射方法

摘要

相对于2D NoC，3D NoC具有更好的集成度和系统性能，是解决低功耗映射的一个可靠途径。在传统粒子群算法(PSOA, particle swarm optimization algorithm)的基础上，提出了一种动态自适应离散粒子群算法(DADPSOA, dynamic adaptive discrete particle swarm optimization algorithm)。该算法基于早熟收敛程度和个体适应度值变化动态调整参数ω，不断靠近最优解；同时对粒子进行合理的解构造，减小了算法时间复杂度。仿真结果表明，与随机映射、遗传算法(GA, genetic algorithm)、PSOA和动态蚁群算法(DACA, dynamic ant colony algorithm)相比，DADPSOA可以缩短执行时间，减小映射结果通信功耗；在面向任务图映射的时候，其通信功耗下降。%Compared to 2D NoC, 3D NoC has better integrated density and system performance, which was a reliable method to solve the problem about low-power mapping. On the basis of the traditional particle swarm optimization algo-rithm (PSOA), a dynamic adaptive discrete particle swarm optimization algorithm (DADPSOA) was proposed . Parame-ter in this algorithm was adjusted dynamically based on the degree of early convergence and the charge of individual adap-tive value to approach the optimal solution. At the same time, the reasonable structure of the particles was made aiming at reducing the time complexity of this algorithm. Experimental results show that comparing with the random mapping, genetic algorithm (GA), PSOA and dynamic ant colony algorithm (DACA), DADPSOA can save the execution time, reduce the communication power consumption of mapping results. The power consumption of the task graph is reduced.