Knowledgeable network-on-chip accelerator for fast and accurate simulations using supervised learning algorithms and multiprocessing. (26th September 2022)

• Record Type: Journal Article
• Authors: Kumar, Anil; Talawar, Basavaraj
• Is Part Of: International journal of intelligent engineering informatics. Volume 10:Number 2(2022)
• Page Start: 160
• Page End: 182
• Publication Date: 2022-09-26
• Subjects: NoC -- network-on-chip -- Floorplan -- performance modelling -- simulation -- machine learning -- prediction -- regression -- decision tree -- Booksim -- performance evaluation -- power -- area -- router -- traffic pattern; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

In a multi-processor system-on-chip (MPSoC) environment, networks-on- chip (NoC) is becoming the de-facto scaling communication technique. One of the most significant techniques used in NoC for analysing and testing new architectures is simulations. Simulation is one of the main tools used in NoC for analysing and testing new architectures. To achieve the best performance vs. cost tradeoff, simulators have become an essential tool. Software simulators are too slow for evaluating large-scale NoCs. To overcome this problem we propose an NoC Accelerator named knowledgeable network-on-chip accelerator (KNoC) which can be used to analyse various NoC architectures. The proposed accelerator is built using machine learning (ML) algorithms and multiprocessing to predict the design parameters of NoCs with a fixed and accurate delay between nodes of large-scale architectures. The KNoC results were compared to the widely used cycle-accurate Booksim simulator. KNoC showed an error rate of less than 6% and an overall speedup of up to 12 Kx.