AI for Edge Computing

G. Jeyaram

ISBN 10: 6209592953 / ISBN 13: 9786209592959

Published by LAP LAMBERT Academic Publishing Feb 2026, 2026

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -AI for Edge Computing presents a concise and systematic introduction to the integration of artificial intelligence with edge computing for real-time and efficient data processing near the data source. The book addresses the limitations of cloud-centric AI systems, such as latency, bandwidth usage, and privacy concerns, and explains how edge intelligence enables faster, reliable, and context-aware decision making. It covers the fundamentals of edge computing architectures, edge hardware platforms, and embedded AI systems, including commonly used devices and accelerators. Lightweight machine learning and deep learning models optimized for edge deployment are discussed along with model compression and optimization techniques. The book also introduces deployment strategies, real-time inference, and widely used frameworks, while highlighting security, privacy, and emerging trends. Designed with an academic focus, this book serves as a valuable resource for students and professionals in artificial intelligence, embedded systems, and edge computing.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 92 pp. Englisch.

Seller Inventory # 9786209592959

Bibliographic Details

• Title: AI for Edge Computing
• Publisher: LAP LAMBERT Academic Publishing Feb 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 6209592953
• ISBN 13: 9786209592959
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 155 grams

About this title

Synopsis

AI for Edge Computing presents a concise and systematic introduction to the integration of artificial intelligence with edge computing for real-time and efficient data processing near the data source. The book addresses the limitations of cloud-centric AI systems, such as latency, bandwidth usage, and privacy concerns, and explains how edge intelligence enables faster, reliable, and context-aware decision making. It covers the fundamentals of edge computing architectures, edge hardware platforms, and embedded AI systems, including commonly used devices and accelerators. Lightweight machine learning and deep learning models optimized for edge deployment are discussed along with model compression and optimization techniques. The book also introduces deployment strategies, real-time inference, and widely used frameworks, while highlighting security, privacy, and emerging trends. Designed with an academic focus, this book serves as a valuable resource for students and professionals in artificial intelligence, embedded systems, and edge computing.