Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
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Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
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Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
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Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : California Books, Miami, FL, Etats-Unis
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Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 126,33
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Edité par Institution of Engineering and Technology, GB, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-Unis
EUR 130,74
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Ajouter au panierHardback. Etat : New. The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications. One of the major challenges is the fetching of data from computing memory and writing it back without experiencing a memory-wall bottleneck. To address such concerns, in-memory computing (IMC) and supporting frameworks have been introduced. In-memory computing methods have ultra-low power and high-density embedded storage. Resistive Random-Access Memory (ReRAM) technology seems the most promising IMC solution due to its minimized leakage power, reduced power consumption and smaller hardware footprint, as well as its compatibility with CMOS technology, which is widely used in industry. In this book, the authors introduce ReRAM techniques for performing distributed computing using IMC accelerators, present ReRAM-based IMC architectures that can perform computations of ML and data-intensive applications, as well as strategies to map ML designs onto hardware accelerators. The book serves as a bridge between researchers in the computing domain (algorithm designers for ML and DL) and computing hardware designers.
Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 121,67
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Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Edité par The Institution of Engineering and Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Edité par Institution of Engineering and Technology, GB, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-Unis
EUR 133,48
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Ajouter au panierHardback. Etat : New. The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications. One of the major challenges is the fetching of data from computing memory and writing it back without experiencing a memory-wall bottleneck. To address such concerns, in-memory computing (IMC) and supporting frameworks have been introduced. In-memory computing methods have ultra-low power and high-density embedded storage. Resistive Random-Access Memory (ReRAM) technology seems the most promising IMC solution due to its minimized leakage power, reduced power consumption and smaller hardware footprint, as well as its compatibility with CMOS technology, which is widely used in industry. In this book, the authors introduce ReRAM techniques for performing distributed computing using IMC accelerators, present ReRAM-based IMC architectures that can perform computations of ML and data-intensive applications, as well as strategies to map ML designs onto hardware accelerators. The book serves as a bridge between researchers in the computing domain (algorithm designers for ML and DL) and computing hardware designers.
Edité par Inst of Engineering & Technology, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 160,79
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Ajouter au panierHardcover. Etat : Brand New. 243 pages. 9.25x6.50x1.00 inches. In Stock.
Edité par INSTITUTION OF ENGINEERING & T, 2021
ISBN 10 : 1839530812 ISBN 13 : 9781839530814
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 145,93
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Ajouter au panierEtat : New. Über den AutorHao Yu is a professor in the School of Microelectronics at Southern University of Science and Technology (SUSTech), China. His main research interests cover energy-efficient IC chip design and mmwave IC design. He i.