  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.

Distributed, Parallel, and Cluster Computing;Artificial Intelligence