Machine learning on commodity tiny devices theory and practice
"This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration...
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| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2023
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| Ausgabe: | First edition |
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| 245 | 1 | 0 | |a Machine learning on commodity tiny devices |b theory and practice |c Song Guo and Qihua Zhou |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Francis Group |c 2023 | |
| 300 | |a xvii, 249 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references (page 187-245) and index | ||
| 520 | 3 | |a "This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system. This book will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems"-- | |
| 653 | 0 | |a Embedded computer systems | |
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Datensatz im Suchindex
| _version_ | 1819314390676013056 |
|---|---|
| any_adam_object | |
| author | Song, Guo ca. 20./21. Jh Zhou, Qihua |
| author_GND | (DE-588)1252101171 |
| author_facet | Song, Guo ca. 20./21. Jh Zhou, Qihua |
| author_role | aut aut |
| author_sort | Song, Guo ca. 20./21. Jh |
| author_variant | g s gs q z qz |
| building | Verbundindex |
| bvnumber | BV048879325 |
| classification_rvk | ST 302 |
| ctrlnum | (OCoLC)1345268863 (DE-599)BVBBV048879325 |
| dewey-full | 004.16 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004.16 |
| dewey-search | 004.16 |
| dewey-sort | 14.16 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | First edition |
| format | Book |
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| id | DE-604.BV048879325 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T09:43:27Z |
| institution | BVB |
| isbn | 9781032374239 9781032374260 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034144094 |
| oclc_num | 1345268863 |
| open_access_boolean | |
| owner | DE-29T DE-521 |
| owner_facet | DE-29T DE-521 |
| physical | xvii, 249 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| spelling | Song, Guo ca. 20./21. Jh. Verfasser (DE-588)1252101171 aut Machine learning on commodity tiny devices theory and practice Song Guo and Qihua Zhou First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2023 xvii, 249 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (page 187-245) and index "This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system. This book will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems"-- Embedded computer systems Machine learning Zhou, Qihua Verfasser aut Erscheint auch als Online-Ausgabe 978-1-003-34022-5 1 |
| spellingShingle | Song, Guo ca. 20./21. Jh Zhou, Qihua Machine learning on commodity tiny devices theory and practice |
| title | Machine learning on commodity tiny devices theory and practice |
| title_auth | Machine learning on commodity tiny devices theory and practice |
| title_exact_search | Machine learning on commodity tiny devices theory and practice |
| title_full | Machine learning on commodity tiny devices theory and practice Song Guo and Qihua Zhou |
| title_fullStr | Machine learning on commodity tiny devices theory and practice Song Guo and Qihua Zhou |
| title_full_unstemmed | Machine learning on commodity tiny devices theory and practice Song Guo and Qihua Zhou |
| title_short | Machine learning on commodity tiny devices |
| title_sort | machine learning on commodity tiny devices theory and practice |
| title_sub | theory and practice |
| work_keys_str_mv | AT songguo machinelearningoncommoditytinydevicestheoryandpractice AT zhouqihua machinelearningoncommoditytinydevicestheoryandpractice |