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Reliability-aware intelligent mapping based on reinforcement learning for networks-on-chips

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Abstract

Designing reliable Networks on Chip (NoCs) is critical, especially with the continuous scaling down of integrated circuit technology, which exposes NoCs to various types of faults. In this paper, a reliability-aware application mapping technique for improving the reliability of heterogeneous NoCs is proposed. It is based on a hybridization of the Multi-Objective Particle Swarp Optimization (MOPSO) algorithm and Reinforcement Learning (RL). At design time, MOPSO and RL perform the optimization of an initial mapping and the prediction of fault-tolerant remapping scenarios, respectively. To teach and to produce an intelligent agent capable of generating an optimal adaptive remapping scheme to address run-time permanent processing element (PE) failures. Two models of RL agents are trained, each based on a different mechanism of task migration: 1) step-based agent and 2) swap-based agent. Experiments were carried out to assess the performance of our innovative technique on various sizes of NoCs, using real benchmarks and varying the levels of heterogeneity and failure in the NoC. The results of the experiments reveal that using RL to solve the reliability problem in NoCs yields interesting results in terms of reliability, energy consumption, execution time, and cost migration.

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  1. Laboratoire des Méthodes de Conception des Systèmes (LMCS), Ecole nationale Supérieure d’Informatique (ESI), BP 68M, 16270, Oued-Smar, Alger, Algeria

    Nassima Kadri, Azzeddine Chenine, Zakaria Laib & Mouloud Koudil

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Kadri, N., Chenine, A., Laib, Z. et al. Reliability-aware intelligent mapping based on reinforcement learning for networks-on-chips. J Supercomput 78, 18153–18188 (2022). https://doi.org/10.1007/s11227-022-04590-5

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