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Design of improved routing algorithm for optical network-on-chip

  • Anandrao Vijay Sutar EMAIL logo und Shilpa P. Gaikwad
Veröffentlicht/Copyright: 10. September 2025
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Abstract

Optical Network-on-Chip (ONoC) is designed to provide energy-efficient and high-performance intercore communication in multicore computers, ONoC is a novel on-chip optical interconnect technology. Sending optical signals via optical interconnects enables low energy consumption, reduced communication delay, and a significant increase in bandwidth capacity. Although considerable research has been conducted on the design of ONoC architecture and routing strategies to ensure correct communication, most current systems employ methods first proposed for electrical interconnections. ONoC communications are in the development stage. The design strategy aims to optimize the utilization of optical waveguide installations by utilizing appropriate channels between Processing Element (PE) nodes. Using an ONoC design is innovative as it serves as a basic component and helps to build bigger ONoC systems. An enhanced adaptive routing method for ONoC is presented in this study. This adaptive method avoids congestion by distributing resources to channels using a buffer allocation technique. This suggested approach chooses the best available way to route the packets by means of adaptive routing and a five-port nonblocking optical router that automatically assigns the buffering resources to many ONoC channels. Proposed technique results are evaluated against X–Y routing on metrics including global average delay and connection usage. The suggested approach is surpassing X–Y routing based on the various standard performance measurements.


Corresponding author: Anandrao Vijay Sutar, Department of Electronics and Communication Engineering, Bharati Vidyapeeth Deemed to be University College of Engineering, Pune, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-06-10
Accepted: 2025-08-04
Published Online: 2025-09-10

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 28.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/joc-2025-0232/html
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