Artificial Intelligence for Edge Computing

Mudhakar Srivatsa, Tarek Abdelzaher, Ting He (Herausgeber)

Buch | Softcover

XIV, 365 Seiten

2024
Springer International Publishing (Verlag)
978-3-031-40789-5 (ISBN)

Inhalt

Part I: Core Problems.- Chapter 1: Neural Network Models for Time Series Data.- Chapter 2: Self-Supervised Learning from Unlabeled IoT Data.- Chapter 3: On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models.- Chapter 4: Out of Distribution Detection.- Chapter 5: Model Compression for Edge Computing.- Part II: Distributed Problems.- Chapter 6: Communication Efficient Distributed Learning.- Chapter 7: Coreset-based Data Reduction for Machine Learning at the Edge.- Chapter 8: Lightweight Collaborative Perception at the Edge.- Chapter 9: Dynamic Placement of Services at the Edge.- Chapter 10: Joint Service Placement and Request Scheduling at the Edge.- Part III: Cross-cutting Thoughts.- Chapter 11: Criticality-based Data Segmentation and Resource Allocation in Machine Inference Pipelines.- Chapter 12: Model Operationalization at Edge Devices.

Produkt-Details

• Erscheinungsdatum: 24.12.2024
• Zusatzinfo: XIV, 365 p. 113 illus., 98 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Maße: 155 x 235 mm
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Technik
• Schlagworte: Coresets • Cyber Physical Systems • Deep Hash Codes • distributed learning • Edge AI • edge computing • Edge Devices • internet of things • machine learning • mobile crowdsensing • Resource Allocation • Small Data Problem • Spiking Networks • Ultra-Low Power AI
• ISBN-10: 3-031-40789-X / 303140789X
• ISBN-13: 978-3-031-40789-5 / 9783031407895
• Zustand: Neuware