Deploying AI on Edge: Advancement and Challenges in Edge Intelligence
Shenyang Institute of Automation · Chinese Academy of Sciences · +1 more institution
Abstract
In recent years, artificial intelligence (AI) has achieved significant progress and remarkable advancements across various disciplines, including biology, computer science, and industry. However, the increasing complexity of AI network structures and the vast number of associated parameters impose substantial computational and storage demands, severely limiting the practical deployment of these models on resource-constrained edge devices. Although edge intelligence methods have been proposed to alleviate the computational and storage burdens, they still face multiple persistent challenges, such as large-scale model deployment, poor interpretability, privacy and security vulnerabilities, and energy efficiency…
Citation impact
- FWCI
- 83.85
- Percentile
- 100%
- References
- 56
Authors
5Topics & keywords
- Enhanced Data Rates for GSM Evolution
- Computer science
- Artificial intelligence