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...
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| 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 |
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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. 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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| 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. 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| 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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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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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000nam a22002652 4500</leader><controlfield tag="001">1904882021</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241007085242.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241007s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781003478713</subfield><subfield code="9">978-1-003-47871-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9788770041027</subfield><subfield code="9">978-87-7004-102-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781040027073</subfield><subfield code="9">978-1-040-02707-3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1904882021</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP108322718</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OAPEN)87610</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)108322718</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">TQ</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">THR</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">TJF</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">UYQ</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Advancing Edge Artificial Intelligence</subfield><subfield code="b">System Contexts</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Erscheinungsort nicht ermittelbar]</subfield><subfield code="b">Taylor & Francis</subfield><subfield code="c">2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (259 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">River Publishers Series in Communications and Networking</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">Open Access</subfield><subfield code="f">Unrestricted online access</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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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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