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Electronics 2016, 5(2), 21; doi:10.3390/electronics5020021

A Software Framework for Rapid Application-Specific Hybrid Photonic Network-on-Chip Synthesis

1
Micron Technology, Non-Volatile Engineering, Longmont, CO 80501, USA
2
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA
This paper is an extended version of our paper published in the proceedings of the IEEE International Symposium on Quality Electronic Design 2012 conference as S. Bahirat, S. Pasricha, “A Particle Swarm Optimization Approach for Synthesizing Application-specific Hybrid Photonic Networks-on-Chip”, IEEE International Symposium on Quality Electronic Design (ISQED), pp. 78-83, March. 2012.
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Author to whom correspondence should be addressed.
Academic Editors: Frédéric Rousseau, Gabriela Nicolescu and Amer Baghdadi
Received: 6 April 2016 / Revised: 27 April 2016 / Accepted: 29 April 2016 / Published: 14 May 2016
View Full-Text   |   Download PDF [6899 KB, uploaded 14 May 2016]   |  

Abstract

Network on Chip (NoC) architectures have emerged in recent years as scalable communication fabrics to enable high bandwidth data transfers in chip multiprocessors (CMPs). These interconnection architectures still need to conquer many challenges, e.g., significant power consumption and high data transfer latencies. Hybrid electro-photonic NoCs have been recently proposed as a solution to mitigate some of these challenges. However, with increasing application complexity, hardware dependencies, and performance variability, optimization of hybrid photonic NoCs requires traversing a massive design space. To date, prior work on software tools for rapid automated NoC synthesis have mainly focused on electrical NoCs. In this article, we propose a novel suite of software tools for effectively synthesizing hybrid photonic NoCs. We formulate and solve the synthesis problem using four search-based optimization heuristics: (1) Ant Colony Optimization (ACO); (2) Particle Swarm Optimization (PSO); (3) Genetic Algorithm (GA); and (4) Simulated Annealing (SA). Our experimental results show significant promise for the ACO and PSO based heuristics. Our novel implementation of PSO achieves an average of 64% energy-delay product improvements over GA and 53% improvement over SA; while our novel ACO implementation achieves 107% energy-delay product improvements over GA and 62% improvement over SA. View Full-Text
Keywords: network-on-chip; photonic interconnects; synthesis algorithms; chip multiprocessors network-on-chip; photonic interconnects; synthesis algorithms; chip multiprocessors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bahirat, S.; Pasricha, S. A Software Framework for Rapid Application-Specific Hybrid Photonic Network-on-Chip Synthesis. Electronics 2016, 5, 21.

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