東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Embedded artificial intelligence = p...

Li, Bin.

FindBook

Google Book

Amazon

博客來

Embedded artificial intelligence = principles, platforms and practices /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Embedded artificial intelligence/ by Bin Li.
其他題名:	principles, platforms and practices /
作者:	Li, Bin.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xi, 260 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Embedded computer systems. -
電子資源:	https://doi.org/10.1007/978-981-97-5038-2
ISBN:	9789819750382

Embedded artificial intelligence = principles, platforms and practices /
Li, Bin.

Embedded artificial intelligenceprinciples, platforms and practices /[electronic resource] :by Bin Li. - Singapore :Springer Nature Singapore :2024. - xi, 260 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9789819750382

Standard No.: 10.1007/978-981-97-5038-2doiSubjects--Topical Terms:

582088
Embedded computer systems.

LC Class. No.: TK7895.E42

Dewey Class. No.: 006.3

Embedded artificial intelligence = principles, platforms and practices /
LDR:03044nmm a2200325 a 4500 001 2374839
003 DE-He213
005 20240906130222.0
006 m d
007 cr nn 008maaau
008 241231s2024 si s 0 eng d
020 $a 9789819750382 $q (electronic bk.)
020 $a 9789819750375 $q (paper)
024 7 $a 10.1007/978-981-97-5038-2 $2 doi
035 $a 978-981-97-5038-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7895.E42
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a TK7895.E42 $b L693 2024
100 1 $a Li, Bin. $3 834070
245 1 0 $a Embedded artificial intelligence $h [electronic resource] : $b principles, platforms and practices / $c by Bin Li.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xi, 260 p. : $b ill. (some col.), digital ; $c 24 cm.
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.
650 0 $a Embedded computer systems. $3 582088
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Embedded Systems. $3 3592715
650 2 4 $a Robotics. $3 519753
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-97-5038-2
950 $a Computer Science (SpringerNature-11645)