Compact and Fast Machine Learning Accelerator for IoT Devices (Computer Architecture and Design Methodologies) - Tapa dura
Libro 6 de 13: Computer Architecture and Design Methodologies
Sinopsis
This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
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De la contraportada
This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
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Compact and Fast Machine Learning Accelerator for Iot Devices
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Compact and Fast Machine Learning Accelerator for IoT Devices (Hardcover)
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Springer Verlag, Singapore, Singapore, 2018
ISBN 10: 981133322X
ISBN 13: 9789811333224
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Hardcover. Condición: new. Hardcover. This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings. This book presents the latest techniques for machine learning based data analytics on IoT edge devices. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9789811333224
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Compact and Fast Machine Learning Accelerator for IoT Devices (Computer Architecture and Design Methodologies)
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Compact and Fast Machine Learning Accelerator for Iot Devices
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Springer-Verlag New York Inc, 2019
ISBN 10: 981133322X
ISBN 13: 9789811333224
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Hardcover. Condición: Brand New. 160 pages. 9.25x6.10x0.63 inches. In Stock. This item is printed on demand. Nº de ref. del artículo: __981133322X
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Compact and Fast Machine Learning Accelerator for IoT Devices
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Springer Nature Singapore, 2018
ISBN 10: 981133322X
ISBN 13: 9789811333224
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Condición: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher | This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings. Nº de ref. del artículo: 33156203/3
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Compact and Fast Machine Learning Accelerator for IoT Devices
Publicado por
Springer Nature Singapore, 2018
ISBN 10: 981133322X
ISBN 13: 9789811333224
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Librería: Buchpark, Trebbin, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings. Nº de ref. del artículo: 33156203/1
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Compact and Fast Machine Learning Accelerator for Iot Devices
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Compact and Fast Machine Learning Accelerator for Iot Devices
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Compact and Fast Machine Learning Accelerator for IoT Devices (Computer Architecture and Design Methodologies)
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Compact and Fast Machine Learning Accelerator for IoT Devices
Publicado por
Springer Nature Singapore Dez 2018, 2018
ISBN 10: 981133322X
ISBN 13: 9789811333224
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings. 160 pp. Englisch. Nº de ref. del artículo: 9789811333224
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