A novel evolutionary-based neuro-fuzzy task scheduling approach to jointly optimize the main design challenges of heterogeneous MPSoCs
This work addresses the design challenges of heterogeneous MPSoCs for embedded systems, but it is incremental as it combines existing techniques (fuzzy neural networks and evolutionary algorithms) for a specific application.
The paper tackles the problem of online task scheduling and mapping in heterogeneous MPSoCs by proposing a method that uses a fuzzy neural network trained with an evolutionary multi-objective algorithm to jointly optimize temperature, power consumption, failure rate, and execution time. The result shows improvements over previous approaches, with average gains of 10.58% in temperature, 9.22% in power consumption, 39.14% in failure rate, and 12.06% in execution time.
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.