Embedded Artificial Intelligence : Principles, Platforms and Practices by Bin Li (2024, Trade Paperback)
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About this product
Product Identifiers
PublisherSpringer
ISBN-109819750377
ISBN-139789819750375
eBay Product ID (ePID)7068285509
Product Key Features
Number of PagesXi, 260 Pages
Publication NameEmbedded Artificial Intelligence : Principles, Platforms and Practices
LanguageEnglish
Publication Year2024
SubjectEngineering (General), Intelligence (Ai) & Semantics, Probability & Statistics / General
TypeTextbook
Subject AreaMathematics, Computers, Technology & Engineering
AuthorBin Li
FormatTrade Paperback
Dimensions
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Dewey Edition23
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal620.0028563
Table Of ContentPART I. PRINCIPLES.- Chapter 1. Embedded Artificial Intelligence.- Chapter 2. Principle of Embedded AI Chips.- Chapter 3. Lightweight Neural Networks.- Chapter 4. Compression of Deep Neural Network.- Chapter 5. Framework for Embedded Neural Network Applications.- Chapter 6. Lifelong Deep Learning.- PART II. PLATFORMS.- Chapter 7. Embedded AI Accelerator Chips.- Chapter 8. Software Framework for Embedded Neural Networks.- PART III. PRACTICES.- Chapter 9. Embedded AI Development Process.- Chapter 10. Optimizing Embedded Neural Network Models.- Chapter 11. Examples of Embedded Neural Network Application.- Chapter 12. Conclusion: Intelligence in Everything.
SynopsisThis book focuses on the emerging topic of embedded artificial intelligence and provides a systematic summary of its principles, platforms, and practices. In the section on principles, it analyzes three main approaches for implementing embedded artificial intelligence: cloud computing mode, local mode, and local-cloud collaborative mode. The book identifies five essential components for implementing embedded artificial intelligence: embedded AI accelerator chips, lightweight neural network algorithms, model compression techniques, compiler optimization techniques, and multi-level cascaded application frameworks. The platform section introduces mainstream embedded AI accelerator chips and software frameworks currently used in the industry. The practical part outlines the development process of embedded artificial intelligence and showcases real-world application examples with accompanying code. As a comprehensive guide to the emerging field of embedded artificial intelligence, the book offers rich and in-depth content, a clear and logical structure, and a balanced approach to both theoretical analysis and practical applications. It provides significant reference value and can serve as an introductory and reference guide for researchers, scholars, students, engineers, and professionals interested in studying and implementing embedded artificial intelligence.
LC Classification NumberQ334-342
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