Advancing Edge Artificial Intelligence : System Contexts
The intersection of AI, the Internet of Things (IoT) and edge computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrus...
Ausführliche Beschreibung
Autor*in: |
Vermesan, Ovidiu [herausgeberIn] Marples, Dave [herausgeberIn] |
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Format: |
E-Book |
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Sprache: |
Englisch |
Erschienen: |
Erscheinungsort nicht ermittelbar: Taylor & Francis ; 2024 |
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Rechteinformationen: |
Open Access Namensnennung - Nicht-kommerziell 4.0 International ; CC BY-NC 4.0 |
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Schlagwörter: |
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Umfang: |
1 Online-Ressource (259 p.) |
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Reihe: |
River Publishers Series in Communications and Networking |
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Links: | |
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ISBN: |
978-1-003-47871-3 978-87-7004-102-7 978-1-040-02707-3 |
Katalog-ID: |
1904882021 |
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9781003478713 978-1-003-47871-3 9788770041027 978-87-7004-102-7 9781040027073 978-1-040-02707-3 (DE-627)1904882021 (DE-599)KEP108322718 (OAPEN)87610 (EBP)108322718 DE-627 ger DE-627 rda eng TQ bicssc THR bicssc TJF bicssc UYQ bicssc Advancing Edge Artificial Intelligence System Contexts [Erscheinungsort nicht ermittelbar] Taylor & Francis 2024 1 Online-Ressource (259 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier River Publishers Series in Communications and Networking Open Access Unrestricted online access star The intersection of AI, the Internet of Things (IoT) and edge computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. 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This book presents some key topics and early thinking from the EdgeAI* project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh. This book aims to empower the reader with the knowledge and insights needed to understand and embrace the transformative power of edge AI technology. The extensively referenced chapters, contributed by experts and thought leaders in the field, are recommended to anyone interested in developing edge AI systems. *Edge AI Technologies for Optimised Performance Embedded Processing"" (EdgeAI) Key Digital Technologies (KDT) Joint Undertaking (JU) European research project. https://https://edge-ai-tech.eu Namensnennung - Nicht-kommerziell 4.0 International CC BY-NC 4.0 cc https://creativecommons.org/licenses/by-nc/4.0/ English Environmental science, engineering and technology Electrical engineering Electronics engineering Artificial intelligence Vermesan, Ovidiu herausgeberin edt Marples, Dave herausgeberin edt https://library.oapen.org/bitstream/id/d35fa76e-47e6-452c-bcb4-78f09f85725c/9781040027042.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/87610 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 460617689X OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 460668268X OLR-OAPEN f z 29-10-24 30 01 0104 4607015415 GBV-OAPEN z 29-10-24 31 01 0027 4606343265 z 29-10-24 34 01 3551 4604619948 OLR-OAPEN zi002 28-10-24 39 01 0547 4607181587 GBV-OAPEN ke 29-10-24 40 01 0007 460601072X OLR-OAPEN xsn 29-10-24 63 01 3401 4607517321 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607683841 OLR-OAPEN z 29-10-24 70 01 0089 4606516364 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604421552 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4604894086 OLR-OAL Vervielfältigungen (z.B. 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spelling |
9781003478713 978-1-003-47871-3 9788770041027 978-87-7004-102-7 9781040027073 978-1-040-02707-3 (DE-627)1904882021 (DE-599)KEP108322718 (OAPEN)87610 (EBP)108322718 DE-627 ger DE-627 rda eng TQ bicssc THR bicssc TJF bicssc UYQ bicssc Advancing Edge Artificial Intelligence System Contexts [Erscheinungsort nicht ermittelbar] Taylor & Francis 2024 1 Online-Ressource (259 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier River Publishers Series in Communications and Networking Open Access Unrestricted online access star The intersection of AI, the Internet of Things (IoT) and edge computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context". This book presents some key topics and early thinking from the EdgeAI* project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh. This book aims to empower the reader with the knowledge and insights needed to understand and embrace the transformative power of edge AI technology. The extensively referenced chapters, contributed by experts and thought leaders in the field, are recommended to anyone interested in developing edge AI systems. *Edge AI Technologies for Optimised Performance Embedded Processing"" (EdgeAI) Key Digital Technologies (KDT) Joint Undertaking (JU) European research project. https://https://edge-ai-tech.eu Namensnennung - Nicht-kommerziell 4.0 International CC BY-NC 4.0 cc https://creativecommons.org/licenses/by-nc/4.0/ English Environmental science, engineering and technology Electrical engineering Electronics engineering Artificial intelligence Vermesan, Ovidiu herausgeberin edt Marples, Dave herausgeberin edt https://library.oapen.org/bitstream/id/d35fa76e-47e6-452c-bcb4-78f09f85725c/9781040027042.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/87610 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 460617689X OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 460668268X OLR-OAPEN f z 29-10-24 30 01 0104 4607015415 GBV-OAPEN z 29-10-24 31 01 0027 4606343265 z 29-10-24 34 01 3551 4604619948 OLR-OAPEN zi002 28-10-24 39 01 0547 4607181587 GBV-OAPEN ke 29-10-24 40 01 0007 460601072X OLR-OAPEN xsn 29-10-24 63 01 3401 4607517321 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607683841 OLR-OAPEN z 29-10-24 70 01 0089 4606516364 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604421552 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4604894086 OLR-OAL Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 28-10-24 110 01 3110 4607347833 OLR-GBV-OAPEN Open Access z 29-10-24 136 01 3526 4605678271 OLR-OAPEN z 29-10-24 147 01 3528 4607851418 OLR-OAPEN frei verfügbar für Europa-Universität Flensburg, Hochschule Flensburg und Zentrale Hochschulbibliothek Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 29-10-24 161 01 0960 460386972X OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 187 01 3520 4604226407 JSTOR Open Access eBook z 28-10-24 213 01 0551 4605345825 OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 230 01 0552 4605511997 OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 283 01 3283 4605844538 OLR-OAL z 29-10-24 293 01 3293 4604044252 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 370 01 4370 4605170375 OLR-FREE Kostenloser Zugriff z 29-10-24 603 01 4603 4603702361 OLR-OAPEN Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers. z 28-10-24 808 01 4808 4606848979 OLR-OAPEN OAPEN Library zg 29-10-24 22 01 0018 Volltextzugang https://library.oapen.org/handle/20.500.12657/87610 23 01 0830 https://library.oapen.org/handle/20.500.12657/87610 30 01 0104 Open Access https://library.oapen.org/handle/20.500.12657/87610 31 01 0027 eBook GBV-OAPEN https://library.oapen.org/handle/20.500.12657/87610 34 01 3551 OpenAccess https://library.oapen.org/handle/20.500.12657/87610 40 01 0007 Volltext, Open Access https://library.oapen.org/handle/20.500.12657/87610 63 01 3401 E-Book https://library.oapen.org/handle/20.500.12657/87610 65 01 0003 Open Access https://library.oapen.org/handle/20.500.12657/87610 65 01 0003 Dieser Titel ist Teil einer Datenbank http://www.bibliothek.uni-regensburg.de/dbinfo/detail.php?titel_id=10728&bib_id=ulb_hal 70 01 0089 https://library.oapen.org/handle/20.500.12657/87610 LF 72 01 0035 https://library.oapen.org/handle/20.500.12657/87610 95 01 3095 https://library.oapen.org/handle/20.500.12657/87610 110 01 3110 Open Access https://library.oapen.org/handle/20.500.12657/87610 136 01 3526 Open Access https://library.oapen.org/handle/20.500.12657/87610 147 01 3528 https://library.oapen.org/handle/20.500.12657/87610 161 01 0960 https://library.oapen.org/handle/20.500.12657/87610 LF 187 01 3520 https://library.oapen.org/handle/20.500.12657/87610 213 01 0551 https://library.oapen.org/handle/20.500.12657/87610 230 01 0552 https://library.oapen.org/handle/20.500.12657/87610 283 01 3283 https://library.oapen.org/handle/20.500.12657/87610 293 01 3293 https://library.oapen.org/handle/20.500.12657/87610 LF 370 01 4370 https://library.oapen.org/handle/20.500.12657/87610 603 01 4603 https://library.oapen.org/handle/20.500.12657/87610 808 01 4808 Volltextzugang https://library.oapen.org/handle/20.500.12657/87610 31 01 0027 00 eBook GBV-OAPEN 22 01 0018 OLR-ZDB-94-OAL 23 01 0830 OLR-OAPEN 30 01 0104 GBV-OAPEN 34 01 3551 OLR-OAPEN 39 01 0547 GBV-OAPEN 39 01 0547 LF 40 01 0007 OLR-OAPEN 63 01 3401 E-Books LF GBV-OAPEN 65 01 0003 OLR-OAPEN 70 01 0089 OLR-OAPEN-OA 72 01 0035 OLR-OAPEN-OA 95 01 3095 OLR-OAL 110 01 3110 OLR-GBV-OAPEN 136 01 3526 OLR-OAPEN 147 01 3528 OLR-OAPEN 161 01 0960 OLR-OAPEN 213 01 0551 OLR-OAPEN 230 01 0552 OLR-OAPEN 283 01 3283 OLR-OAL 293 01 3293 OLR-OAPEN 370 01 4370 OLR-FREE 370 01 4370 olr-ebook GBV-OAPEN 603 01 4603 OLR-OAPEN 808 01 4808 OLR-OAPEN |
allfields_unstemmed |
9781003478713 978-1-003-47871-3 9788770041027 978-87-7004-102-7 9781040027073 978-1-040-02707-3 (DE-627)1904882021 (DE-599)KEP108322718 (OAPEN)87610 (EBP)108322718 DE-627 ger DE-627 rda eng TQ bicssc THR bicssc TJF bicssc UYQ bicssc Advancing Edge Artificial Intelligence System Contexts [Erscheinungsort nicht ermittelbar] Taylor & Francis 2024 1 Online-Ressource (259 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier River Publishers Series in Communications and Networking Open Access Unrestricted online access star The intersection of AI, the Internet of Things (IoT) and edge computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context". This book presents some key topics and early thinking from the EdgeAI* project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh. This book aims to empower the reader with the knowledge and insights needed to understand and embrace the transformative power of edge AI technology. The extensively referenced chapters, contributed by experts and thought leaders in the field, are recommended to anyone interested in developing edge AI systems. *Edge AI Technologies for Optimised Performance Embedded Processing"" (EdgeAI) Key Digital Technologies (KDT) Joint Undertaking (JU) European research project. https://https://edge-ai-tech.eu Namensnennung - Nicht-kommerziell 4.0 International CC BY-NC 4.0 cc https://creativecommons.org/licenses/by-nc/4.0/ English Environmental science, engineering and technology Electrical engineering Electronics engineering Artificial intelligence Vermesan, Ovidiu herausgeberin edt Marples, Dave herausgeberin edt https://library.oapen.org/bitstream/id/d35fa76e-47e6-452c-bcb4-78f09f85725c/9781040027042.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/87610 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 460617689X OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 460668268X OLR-OAPEN f z 29-10-24 30 01 0104 4607015415 GBV-OAPEN z 29-10-24 31 01 0027 4606343265 z 29-10-24 34 01 3551 4604619948 OLR-OAPEN zi002 28-10-24 39 01 0547 4607181587 GBV-OAPEN ke 29-10-24 40 01 0007 460601072X OLR-OAPEN xsn 29-10-24 63 01 3401 4607517321 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607683841 OLR-OAPEN z 29-10-24 70 01 0089 4606516364 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604421552 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4604894086 OLR-OAL Vervielfältigungen (z.B. 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Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context". This book presents some key topics and early thinking from the EdgeAI* project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh. This book aims to empower the reader with the knowledge and insights needed to understand and embrace the transformative power of edge AI technology. 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The intersection of AI, the Internet of Things (IoT) and edge computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context". This book presents some key topics and early thinking from the EdgeAI* project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh. This book aims to empower the reader with the knowledge and insights needed to understand and embrace the transformative power of edge AI technology. The extensively referenced chapters, contributed by experts and thought leaders in the field, are recommended to anyone interested in developing edge AI systems. *Edge AI Technologies for Optimised Performance Embedded Processing"" (EdgeAI) Key Digital Technologies (KDT) Joint Undertaking (JU) European research project. https://https://edge-ai-tech.eu |
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The intersection of AI, the Internet of Things (IoT) and edge computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context". This book presents some key topics and early thinking from the EdgeAI* project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh. This book aims to empower the reader with the knowledge and insights needed to understand and embrace the transformative power of edge AI technology. The extensively referenced chapters, contributed by experts and thought leaders in the field, are recommended to anyone interested in developing edge AI systems. *Edge AI Technologies for Optimised Performance Embedded Processing"" (EdgeAI) Key Digital Technologies (KDT) Joint Undertaking (JU) European research project. https://https://edge-ai-tech.eu |
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The intersection of AI, the Internet of Things (IoT) and edge computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context". This book presents some key topics and early thinking from the EdgeAI* project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh. This book aims to empower the reader with the knowledge and insights needed to understand and embrace the transformative power of edge AI technology. The extensively referenced chapters, contributed by experts and thought leaders in the field, are recommended to anyone interested in developing edge AI systems. *Edge AI Technologies for Optimised Performance Embedded Processing"" (EdgeAI) Key Digital Technologies (KDT) Joint Undertaking (JU) European research project. https://https://edge-ai-tech.eu |
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Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context". This book presents some key topics and early thinking from the EdgeAI* project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh. This book aims to empower the reader with the knowledge and insights needed to understand and embrace the transformative power of edge AI technology. 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