通过移动边缘计算为生成式人工智能赋能

Laha Ale, Ning Zhang, Scott A. King, Dajiang Chen
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摘要

生成式人工智能(GenAI)为制造、营销、医疗、教育和工作辅助等物联网的各个领域带来了深刻变革。然而,计算密集型和高度复杂的 GenAI 模型的激增给服务器和中央网络能力带来了巨大挑战。为了有效渗透到我们生活的方方面面,GenAI 在很大程度上依赖于移动边缘计算。在这篇 "视角 "文章中,我们首先介绍了边缘设备上的 GenAI 应用,强调了其彻底改变我们日常生活的潜在能力。然后,我们概述了在边缘设备上部署 GenAI 所面临的挑战,并提出了有效解决这些障碍的可行方案。最后,我们介绍了一种能够减少响应延迟、提高效率、加强安全和隐私保护以及节约能源的智能移动边缘计算范例,为不同 GenAI 模型的可持续高效应用开辟了道路。将生成式人工智能应用于移动设备有可能实现集成化、个性化和情境感知体验。然而,计算能源需求是一项挑战。本视角文章介绍了一种智能移动边缘计算范例,用于在物联网系统中实施生成式人工智能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Empowering generative AI through mobile edge computing
Generative artificial intelligence (GenAI) has brought about profound transformations across the diverse domains of the Internet of Things such as manufacturing, marketing, medicine, education and work assistance. However, the proliferation of computationally intensive and highly complex GenAI models poses substantial challenges to servers and central network capacities. To effectively permeate various facets of our lives, GenAI heavily relies on mobile edge computing. In this Perspective article, we first introduce GenAI applications on edge devices highlighting its potential capacity to revolutionize our everyday life. We then outline the challenges associated with deploying GenAI on edge devices and present possible solutions to effectively address these obstacles. Finally, we introduce an intelligent mobile edge computing paradigm able to reduce response latency, improve efficiency, strengthen security and privacy preservation and conserve energy, opening the way to a sustainable and efficient application of the different GenAI models. The application of generative artificial intelligence to mobile devices has the potential to enable integrated, personalized, contextually aware experiences. However, the computational energy demand is challenging. This Perspective article introduces an intelligent mobile edge computing paradigm for the implementation of generative artificial intelligence on the Internet of Things system.
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