Compact and Fast Machine Learning Accelerator for Iot Devices - Rilegato
Libro 6 di 13: Computer Architecture and Design Methodologies
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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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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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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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. Condizione: 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. Codice articolo 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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ISBN 13: 9789811333224
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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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Condizione: 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. Codice articolo 33156203/3
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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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Da: Buchpark, Trebbin, Germania
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Condizione: 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. Codice articolo 33156203/1
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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 Nature Singapore Dez 2018, 2018
ISBN 10: 981133322X
ISBN 13: 9789811333224
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Buch. Condizione: 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. Codice articolo 9789811333224
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