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010   $a981-9750-38-5
024 7 $a10.1007/978-981-97-5038-2
035   $a(MiAaPQ)EBC31650801
035   $a(Au-PeEL)EBL31650801
035   $a(CKB)34843071100041
035   $a(DE-He213)978-981-97-5038-2
035   $a(EXLCZ)9934843071100041
100   $a20240906d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEmbedded Artificial Intelligence $ePrinciples, Platforms and Practices /$fby Bin Li
205   $a1st ed. 2024.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024.
215   $a1 online resource (262 pages)
311 08$a981-9750-37-7 
327   $aPART 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.
330   $aThis 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.
606   $aArtificial intelligence
606   $aComputational intelligence
606   $aMachine learning
606   $aEmbedded computer systems
606   $aRobotics
606   $aArtificial Intelligence
606   $aComputational Intelligence
606   $aMachine Learning
606   $aEmbedded Systems
606   $aRobotics
615  0$aArtificial intelligence.
615  0$aComputational intelligence.
615  0$aMachine learning.
615  0$aEmbedded computer systems.
615  0$aRobotics.
615 14$aArtificial Intelligence.
615 24$aComputational Intelligence.
615 24$aMachine Learning.
615 24$aEmbedded Systems.
615 24$aRobotics.
676   $a006.3
700   $aLi$b Bin$0857556
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910886999803321
996   $aEmbedded Artificial Intelligence$94236284
997   $aUNINA