论文标题
一种新型基于进化的神经模糊任务调度方法，可以共同优化异质MPSOC的主要设计挑战
A novel evolutionary-based neuro-fuzzy task scheduling approach to jointly optimize the main design challenges of heterogeneous MPSoCs
论文作者
论文摘要
在本文中，提出了一种基于基于模糊神经网络（FNN）的在线任务调度和映射方法，该方法通过进化多目标算法（NSGA-II）学习，以共同优化异质MPSOC的主要设计挑战。在这种方法中，首先，通过考虑MPSOC的主要设计挑战，包括温度，功率消耗，故障率和执行时间，使用基于NSGA-II的优化引擎对FNN参数进行训练，该培训数据集由各种尺寸的不同应用程序图组成。接下来，训练有素的FNN被用作在线任务调度程序，可以共同优化异质MPSOC中的主要设计挑战。由于传感器测量的不确定性以及计算模型与现实之间的差异，因此在在线调度程序中应用模糊神经网络是有利的。该方法的性能与几个实验中的一些先前的启发式，元式 - 高 - 神秘主义和基于规则的方法进行了比较。基于这些实验，我们提出的方法优于优化所有设计标准的相关研究。它对相关的启发式和元启发式方法的改善估计为10.58％，功率消耗9.22％，失败率为39.14％，执行时间为12.06％。此外，考虑到FNN的可解释性质，展示了拟议方法的经常被解雇的模糊规则。
In this paper, an online task scheduling and mapping method based on a fuzzy neural network (FNN) learned by an evolutionary multi-objective algorithm (NSGA-II) to jointly optimize the main design challenges of heterogeneous MPSoCs is proposed. In this approach, first, the FNN parameters are trained using an NSGA-II-based optimization engine by considering the main design challenges of MPSoCs including temperature, power consumption, failure rate, and execution time on a training dataset consisting of different application graphs of various sizes. Next, the trained FNN is employed as an online task scheduler to jointly optimize the main design challenges in heterogeneous MPSoCs. Due to the uncertainty in sensor measurements and the difference between computational models and reality, applying the fuzzy neural network is advantageous in online scheduling procedures. The performance of the method is compared with some previous heuristic, meta-heuristic, and rule-based approaches in several experiments. Based on these experiments our proposed method outperforms the related studies in optimizing all design criteria. Its improvement over related heuristic and meta-heuristic approaches are estimated 10.58% in temperature, 9.22% in power consumption, 39.14% in failure rate, and 12.06% in execution time, averagely. Moreover, considering the interpretable nature of the FNN, the frequently fired extracted fuzzy rules of the proposed approach are demonstrated.
