Comparative Analysis of Simulators for Optical Network-on-Chip (ONoC)

Zhang, Hao; Chen, Yawen; Huang, Zhiyi; Xia, Chengpeng; Liang, Jianguo; Gu, Huaxi

doi:10.1109/paap54281.2021.9720307

Cited by 3 publications

(1 citation statement)

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“…With the use of power estimation of hundreds of various network configurations, inefficient or infeasible networks can be immediately detected and eliminated before a thorough analysis is performed [7]. NoC deals with a power issue known as leakage power, which is becoming more prevalent as technology becomes smaller.…”

Section: Introductionmentioning

confidence: 99%

Efficient Power Estimation Using DSENT for 3D-Mesh on Chip Optic Communication Network

Ahmed,

Ambudka,

Yadav

2024

ISI

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The Network-on-Chip (NoC) stands as a potent solution for achieving heightened performance, efficient communication, and dependability in the migration of Very-Large Scale Integration (VLSI) architecture toward deep submicron technology, when contrasted with conventional connectivity networks. Considerable research endeavours have been allocated to diverse facets of NoCs, encompassing topology, routing algorithms, traffic behaviours, power management, and fundamental mapping. This paper explores the power consumption efficiency of Parameterized Path-Based, Randomized, Oblivious, Minimal for 3D Mesh (PROM3D) routing and ZXY routing algorithms for various traffic patterns like transpose, bit shuffle, and random traffic with the help of the integrated DSENT network model. The PROM3D routing algorithm selects a path randomly from all possible minimal pathways between the source and destination, whereas ZXY is a layer-based routing method. The Design Space Exploration of Network (DSENT) tool is used with the NoC Interconnect Routing and Applications Modeling (NIRGAM) simulator in experiments to measure the power consumption. The findings indicate that, within the 3D-Mesh environment, the ZXY routing algorithm exhibits a 0.02% of variation in power consumption while in saturation on varying loads for various traffic patterns in comparison to the PROM3D algorithm.

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Section: Introductionmentioning

confidence: 99%