Network for AI: Efficient Mapping with Fine Grain OTN for AI Computing Services via Entropy-Coupled Incremental Learning
We propose an entropy-coupled incremental learning (ECIL) mapping framework for fine grain optical transport network (fgOTN), achieving significant improvements in convergence, latency, and resource utilization, thereby optimizing AI computing service integration and processing efficiency.
Authors
- Hui Yang (ORCID: https://orcid.org/0009-0005-2034-7735)
- Yang Zhao (ORCID: https://orcid.org/0000-0002-5140-5126)
- Mohamed Cheriet (ORCID: https://orcid.org/0000-0002-5246-7265)
- Tiankuo Yu (ORCID: https://orcid.org/0000-0001-5279-1033)
- Sunling Gong (ORCID: https://orcid.org/0000-0003-3380-0859)
- Qiuyan Yao (ORCID: https://orcid.org/0000-0001-8753-6489)
- Jie Zhang (ORCID: https://orcid.org/0000-0002-3354-0690)
- Zepeng Zhang (ORCID: https://orcid.org/0000-0002-5994-5848)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- China Mobile (China) (CN)
- State Key Laboratory of Information Photonics and Optical Communications
- École de Technologie Supérieure (CA)
Publication Details
- Published
- 2025-06-29
- DOI
- https://doi.org/10.23919/oecc/psc62146.2025.11109275
- Primary Topic
- Machine Learning and ELM
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundamental Research Funds for the Central Universities