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Feasibility Study of Edge Computing Empowered by Artificial Intelligence—A Quantitative Analysis Based on Large Models

Authors :
Yan Chen
Chaonan Wu
Runqi Sui
Jingjia Zhang
Source :
Big Data and Cognitive Computing, Vol 8, Iss 8, p 94 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The advancement of artificial intelligence (AI) demands significant data and computational resources that have an adverse impact on the environment. To address this issue, a novel computing architecture that is both energy efficient and eco-friendly is urgently required. Edge computing has emerged as an increasingly popular solution to this problem. In this study, we explore the development history of edge computing and AI and analyze the potential of model quantization to link AI and edge computing. Our comparative analysis demonstrates that the quantization approach can effectively reduce the model’s size and accelerate model inference while maintaining its functionality, thereby enabling its deployment on edge devices. This research serves as a valuable guide and reference for future advancements in edge AI.

Details

Language :
English
ISSN :
25042289
Volume :
8
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Big Data and Cognitive Computing
Publication Type :
Academic Journal
Accession number :
edsdoj.43c74e4b0c3645acaca5db527a4dd545
Document Type :
article
Full Text :
https://doi.org/10.3390/bdcc8080094