JOURNAL ARTICLE
A Novel Hybrid Hexagonal Star Topology for On-Chip Interconnection Networks.
Published In: Journal of Circuits, Systems & Computers, 2023, v. 32, n. 5. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lakshmi Kiranmai, V.; Srinivasarao, B. K. N. 3 of 3
Abstract
Network-on-Chip (NoC) is an emerging and efficient on-chip interconnection network. NoC is expected to be the communication backbone of next-generation Multi-processor System-on-Chip (MPSoC) architectures. Topology is a crucial design aspect of NoC, as it affects the performance of the interconnection network. This paper proposes a novel, scalable, hybrid Hexagonal Star (HS) topology for on-chip interconnection networks. Properties of the proposed topology have been explored and compared with those of Mesh, Torus and Honeycomb Mesh topologies. The performance of the Hexagonal Star topology has been evaluated and compared with that of Mesh topology for different scenarios. The comparative studies of topological properties have indicated that the proposed topology can be a potential choice for on-chip interconnection networks. The simulation results have shown that the proposed topology outperforms Mesh topology in terms of latency for low traffic loads. For different traffic patterns and traffic loads, HS topology has registered a reduction of packet latency ranging from 15% to 50% and from 9% to 23% for 18 nodes and 32 nodes, respectively, compared to Mesh topology. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Circuits, Systems & Computers. 2023/03, Vol. 32, Issue 5, p1
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2023
- ISSN:0218-1266
- DOI:10.1142/S0218126623500767
- Accession Number:162382844
- Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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