Abstract
Proposed herein are techniques that facilitate the dynamic execution of Artificial Intelligence/Machine Learning (AI/ML) models at a packet level across distributed nodes, which is distinguished from traditional centralized AI/ML deployment or static configurations. The techniques have the potential to enhance network efficiency, security, and adaptability by enabling real-time, distributed AI/ML inference that is directly activated by network traffic patterns and control signals embedded within packets.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Sankaranarayanan, Madhan; Rajamanickam, Jaganbabu; Li, Yitong; Paramasivan, Ganesh; and Suri, Shivani, "DYNAMIC PACKET-LEVEL AI/ML MODEL INSTRUCTION THROUGH EXTENSION HEADERS FOR DISTRIBUTED NETWORK DEVICES", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9517