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GANNoC: A Framework for Automatic Generation of NoC Topologies using Generative Adversarial Networks

Maxime Mirka 1 Maxime France-Pillois 1 Gilles Sassatelli 1 Abdoulaye Gamatié 1 
1 ADAC - ADAptive Computing
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : We propose GANNoC, a framework for automatic generation of customized Network-on-Chip (NoC) topologies, which exploits generative adversarial networks (GANs) learning capabilities. We define the problem of NoC generation as a graph generation problem, and train a GAN to produce such graphs. We further present a Reward-WGAN (RWGAN) architecture, based on the Wasserstein GAN (WGAN). It is coupled to a reward network enabling to steer the resulting generative system towards topologies having desired properties. We illustrate this capability through a case study aimed at producing topologies with a specific number of physical connections. After training, the generative network produces unique topologies with a 36% improvement regarding the number of connections, when compared to those found in the training dataset. NoCs' performance assessment is carried out using the Ratatoskr 3D-NoC simulator with state-of-the-art characteristics. Results suggest interesting opportunities in learning correlations between intrinsic NoC features and resulting performance.
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Submitted on : Friday, January 22, 2021 - 3:51:29 PM
Last modification on : Friday, August 5, 2022 - 3:02:14 PM


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Maxime Mirka, Maxime France-Pillois, Gilles Sassatelli, Abdoulaye Gamatié. GANNoC: A Framework for Automatic Generation of NoC Topologies using Generative Adversarial Networks. RAPIDO 2021 - 13th Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, Jan 2021, Budapest, Hungary. pp.51-58, ⟨10.1145/3444950.3447283⟩. ⟨lirmm-03107918v2⟩



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