面向温度优化的片上网络任务调度方法

随着片上网络规模的扩大和研究的逐步深入，如何将芯片上众多的任务进行合理的调度成为系统温度优化的关键之一。针对片上网络任务调度问题， 提出一种基于最短曼哈顿距离的任务调度SMDS方案。该策略充分考虑核通信图中通信节点对之间最短曼哈顿路径，通过搜索算法寻找任务调度的目的节点，使用模拟退火算法确定任务调度对。实验结果显示，与传统的分布式任务调度 DTM策略相比，针对6*6、8*8和10*10的拓扑结构，SMDS实验方案在迁移次数方面的平均优化率分别为2208%、21.74%和23.02%。在平均跳数方面的平均优化率分别为24.04%、29.18%和23.04%，实现了系统温度优化。

A task scheduling method for network-on-chip temperature optimization

As the scale of network-on-chip (NoC) expands and related research goes further, task scheduling becomes a key problem for optimizing system temperature. For the task scheduling problem of NoC, we present a task scheduling method based on the shortest Manhattan distance. The method fully considers the shortest Manhattan path between the pairs of communication nodes in a core communication graph, uses a search algorithm to find the destination node for scheduling tasks, and determines the task scheduling pair with simulated annealing algorithm. SMDS experimental results indicate that, compared with the traditional distributed task migration (DTM) strategy, for 6*6, 8*8 and 10*10 topologies, the optimization for the times of migration is reduced by 22.08%, 21.74% and 23.02%, respectively, and the average optimization rate for average hop count is 24.04%, 29.18% and 23.04%, which achieves system temperature optimization.