A novel evolutionary-based neuro-fuzzy task scheduling approach to jointly optimize the main design challenges of heterogeneous MPSoCs
Athena Abdi, Armin Salimi-Badr

TL;DR
This paper introduces an online task scheduling method for heterogeneous MPSoCs using a fuzzy neural network trained by an evolutionary multi-objective algorithm, effectively optimizing temperature, power, failure rate, and execution time.
Contribution
It presents a novel neuro-fuzzy scheduling approach trained with NSGA-II, improving multiple design challenges simultaneously in heterogeneous MPSoCs.
Findings
Outperforms previous heuristic and meta-heuristic methods in all optimization criteria.
Achieves approximately 10-40% improvements in key design metrics.
Provides interpretable fuzzy rules for scheduling decisions.
Abstract
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…
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Taxonomy
TopicsEvolutionary Algorithms and Applications
