Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading
Document Type
Article
Publication Date
1-1-2024
Abstract
From AI-assisted art creation to large language model (LLM)-powered ChatGPT, AI-generated contents and services are becoming a transforming force. It calls for the telecom industry to embrace the prospects of AIGC services and face the unique challenges posed by incorporating generative model services into the AI-native 6G wireless network paradigm. We propose enabling AIGC inference services on mobile devices by optimizing MEC-device computing offloading, through which AIGC task latency is minimized by reinforcement learning based policy agent in a computing resource constrained and bandwidth limited wireless environment. Simulation results are presented to demonstrate the performance advantage.
Identifier
85209464414 (Scopus)
Publication Title
IEEE Networking Letters
External Full Text Location
https://doi.org/10.1109/LNET.2024.3490954
e-ISSN
25763156
First Page
232
Last Page
236
Issue
4
Volume
6
Recommended Citation
Zhou, Changshi; Liu, Weiqi; Han, Tao; and Ansari, Nirwan, "Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading" (2024). Faculty Publications. 804.
https://digitalcommons.njit.edu/fac_pubs/804
