Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading

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.

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Publication Details

Journal
IEEE Networking Letters
Published
2024-11-04
DOI
https://doi.org/10.1109/lnet.2024.3490954
Citations
5
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
1.74
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article

Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading

Nirwan Ansari, Changshi Zhou, Weiqi Liu, Tao Han
5 citations
IEEE Networking Letters
IoT and Edge/Fog Computing
1.74
article

Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading

Nirwan Ansari, Changshi Zhou, Weiqi Liu, Tao Han
article en
5 citations

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.

IEEE Networking LettersVol. 6(4)
New Jersey Institute of Technology (US), Auburn University at Montgomery (US)
Openalex Percentile: Top 14%
IoT and Edge/Fog Computing
1.74
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