Edge inference
From HandWiki
Short description: Execution of machine learning models on edge devices
Edge inference is the process of running machine learning or deep learning models on local devices (edge devices) such as smartphones, IoT devices, embedded systems, and edge servers instead of centralized cloud computing infrastructure. [1][2][3][4] A key feature of edge computing is edge inference, which allows for real-time data processing, low latency, and improved privacy by reducing the amount of data sent to remote servers.[5]
See also
References
- ↑ Shi, Weisong; Cao, Jie; Zhang, Quan; Li, Youhuizi; Xu, Lanyu (2016). "Edge Computing: Vision and Challenges". IEEE Internet of Things Journal 3 (5): 637–646. doi:10.1109/JIOT.2016.2579198. Bibcode: 2016IITJ....3..637S.
- ↑ Ngo, Dat; Park, Hyun-Cheol; Kang, Bongsoon (2025-06-19). "Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments" (in en). Electronics 14 (12): 2495. doi:10.3390/electronics14122495. ISSN 2079-9292.
- ↑ "What is AI inference? How it works and examples" (in en-US). https://cloud.google.com/discover/what-is-ai-inference.
- ↑ "What is AI inference at the edge, and why is it important for businesses?" (in en). 2024-07-22. https://www.techradar.com/pro/what-is-ai-inference-at-the-edge-and-why-is-it-important-for-businesses.
- ↑ Satyanarayanan, Mahadev (2017). "The Emergence of Edge Computing". Computer 50 (1): 30–39. doi:10.1109/MC.2017.9. Bibcode: 2017Compr..50a..30S.
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