Embedded Artificial Intelligence Principles, Platforms and Practices /
This 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 mo...
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| Awdur Corfforaethol: | |
| Fformat: | Electronig eLyfr |
| Iaith: | Saesneg |
| Cyhoeddwyd: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2024.
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| Rhifyn: | 1st ed. 2024. |
| Pynciau: | |
| Mynediad Ar-lein: | https://doi.org/10.1007/978-981-97-5038-2 |
| Tagiau: |
Ychwanegu Tag
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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| LEADER | 00000nam a22000005i 4500 | ||
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| 100 | 1 | |a Li, Bin. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Embedded Artificial Intelligence |h [electronic resource] : |b Principles, Platforms and Practices / |c by Bin Li. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XI, 260 p. 146 illus.). |b 33 illus. in color. | ||
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| 341 | 0 | |b Table of contents navigation |2 onix | |
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| 505 | 0 | |a PART 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. | |
| 520 | |a This 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Embedded computer systems. | |
| 650 | 0 | |a Robotics. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Embedded Systems. |
| 650 | 2 | 4 | |a Robotics. |
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| 776 | 0 | 8 | |i Printed edition: |z 9789819750375 |
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| 856 | 4 | 0 | |u https://doi.org/10.1007/978-981-97-5038-2 |
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| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||