通过最佳MEC-Device卸载在6G中部署设备上AIGC推理服务

Changshi Zhou;Weiqi Liu;Tao Han;Nirwan Ansari
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引用次数: 0

摘要

从人工智能辅助的艺术创作到大型语言模型(LLM)驱动的ChatGPT,人工智能生成的内容和服务正在成为一股变革力量。它呼吁电信行业拥抱AIGC服务的前景,并面对将生成模型服务纳入人工智能原生6G无线网络范式所带来的独特挑战。我们提出通过优化MEC-device计算卸载在移动设备上启用AIGC推理服务,在计算资源受限和带宽有限的无线环境下,通过基于强化学习的策略代理最小化AIGC任务延迟。仿真结果验证了该方法的性能优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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