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|Title:||ELBA-NoC: Ensemble learning-based accelerator for 2D and 3D network-on-chip architectures|
|Citation:||International Journal of Computational Science and Engineering Vol. 23 , 4 , p. 319 - 335|
|Abstract:||Network-on-chips (NoCs) have emerged as a scalable alternative to traditional bus and point-to-point architectures, it has become highly sensitive as the number of cores increases. Simulation is one of the main tools used in NoC for analysing and testing new architectures. To achieve the best performance vs. cost trade-off, simulators have become an essential tool. Software simulators are too slow for evaluating large scale NoCs. This paper presents a framework which can be used to analyse overall performance of 2D and 3D NoC architectures which is fast and accurate. This framework is named as ensemble learning-based accelerator (ELBA-NoC) which is built using random forest regression algorithm to predict parameters of NoCs. On 2D, 3D NoC architectures, ELBA-NoC was tested and the results obtained were compared with extensively used Booksim NoC simulator. The framework showed an error rate of less than 5% and an overall speedup of up to 16 K×. Copyright © 2020 Inderscience Enterprises Ltd.|
|Appears in Collections:||1. Journal Articles|
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