Edge AI and Intelligent Computing : Machine Learning, Embedded Systems, Edge Devices, and Real-Time AI Applications
Language: English
Published by Amazon Digital Services LLC - Kdp Aug 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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- Title
- Edge AI and Intelligent Computing : Machine Learning, Embedded Systems, Edge Devices, and Real-Time AI Applications
- Author
- Saumya Srivastava
- Publisher
- Amazon Digital Services LLC - Kdp Aug 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798194740581
- Item weight
- 318 grams
- Dimensions
- 229x152x12 mm
- Series
- Book 2 of 5: Artificial Intelligence
This book was written to fill that gap. It is intended for advanced undergraduate students, graduate students, and practicing engineers who already possess some grounding in either machine learning or embedded systems — but not necessarily both — and who need a single, coherent, technically rigorous treatment that connects the two. Throughout, the book deliberately privileges engineering trade-offs and system-level reasoning over any single technology's implementation details, on the conviction that specific hardware platforms and software frameworks will continue to evolve rapidly, while the underlying analytical framework for reasoning about latency, bandwidth, energy, memory, and real-time determinism will remain durably useful regardless of which specific technologies a reader ultimately works with.
The book's structure reflects this philosophy. Part I establishes shared foundations across machine learning and embedded systems, deliberately written to be accessible to readers strong in one domain but less familiar with the other. Part II develops the core technology stack — hardware accelerators, model compression, software toolchains, and real-time operating systems — that connects a trained model to a deployed, resource-constrained device. Part III surveys applications in substantial technical depth, from computer vision through industrial and smart-city deployments, always grounding the discussion in the foundational and technological material developed earlier. Part IV addresses the advanced and forward-looking concerns — security, federated learning, energy management, and emerging trends — that increasingly separate mature, production-grade edge AI engineering from early-stage prototyping.
Every chapter includes original diagrams designed specifically for this book to illustrate its core architectural and conceptual content, tables summarizing comparative technical trade-offs, worked case studies drawn from realistic deployment scenarios, chapter-ending review questions suitable for coursework or self-assessment, and a curated reference list pointing toward the foundational and current literature underlying each chapter's content. It is my hope that readers — whether working through this book cover to cover as a course text, or consulting individual chapters as a technical reference during a specific project — come away with both the conceptual vocabulary and the practical engineering judgment needed to design edge AI systems that are not merely accurate in isolation, but genuinely deployable: correctly sized to their hardware, defensible under real-time constraints, secure against realistic threats, and honest about their energy budget.
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