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Analysis of power consumption on switch fabrics in network routers
Open accessAbstract
In this paper, we introduce a framework to estimate the power consumption on switch fabrics in network routers. We propose different modeling methodologies for node switches, internal buffers and interconnect wires inside switch fabric architectures. A simulation platform is also implemented to trace the dynamic power consumption with bit-level accuracy. Using this framework, four switch fabric architectures are analyzed under different traffic throughput and different numbers of ingress/egress ports. This framework and analysis can be applied to the architectural exploration for low power high performance network router designs.
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Cited by
Energy-aware mapping for tile-based NoC architectures under performance constraints
Hu, J., & Marculescu, R. (2003) · Proceedings of the 2003 conference on Asia South Pacific design automation - ASPDAC · doi:10.1145/1119772.1119818 · Cited by 580
Reconfigurable router using RLBS algorithm
Oommen, D., & Pradeep, C. (2012) · 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2012.6416560
Metadata
- Authors
- Ye, T. T., Micheli, G. D., & Benini, L.
- Venue
- Proceedings of the 39th conference on Design automation - DAC '02
- Publisher
- ACM Press
- Published
- 2002
- Pages
- 524
- ISSN
- 0738-100X (ISSN-L)
- OpenAlex
- W2103066735
- Cited by
- 407
- References
- 0
- FWCI
- 9.82
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ye, T. T., Micheli, G. D., & Benini, L. (2002). Analysis of power consumption on switch fabrics in network routers. Proceedings of the 39th conference on Design automation - DAC '02. https://doi.org/10.1145/513918.514051
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.