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Verlag: Morgan & Claypool Publishers, 2016
ISBN 10: 1627052941 ISBN 13: 9781627052948
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Sprache: Englisch
Verlag: Morgan & Claypool (Synthesis Lectures on Emerging Engineering Technologies), [San Raphael, CA], 2017
ISBN 10: 1627052941 ISBN 13: 9781627052948
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Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2021
ISBN 10: 1839530812 ISBN 13: 9781839530814
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Hardback. Zustand: 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.
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Verlag: The Institution of Engineering and Technology, 2021
ISBN 10: 1839530812 ISBN 13: 9781839530814
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In den WarenkorbHardback. Zustand: 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.
Sprache: Englisch
Verlag: Institution of Engineering and Technology, GB, 2021
ISBN 10: 1839530812 ISBN 13: 9781839530814
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In den WarenkorbHardback. Zustand: 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.
Sprache: Englisch
Verlag: Inst of Engineering & Technology, 2021
ISBN 10: 1839530812 ISBN 13: 9781839530814
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ISBN 10: 1839530812 ISBN 13: 9781839530814
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In den WarenkorbZustand: 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.
Sprache: Englisch
Verlag: Institution of Engineering and Technology, GB, 2021
ISBN 10: 1839530812 ISBN 13: 9781839530814
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In den WarenkorbHardback. Zustand: 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.
Verlag: Moscow, 1976
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Hardcover. Zustand: Good. In Russian. Leibin, Valery Moiseevich. Philosophy of Social Criticism in the United States. Moscow: Science, 1976. All images are for identification of editions only. Several books of the same edition may be available. Please feel free to request photos of available books.SKU6940719.
Verlag: Moscow, 1977
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Hardcover. Zustand: Good. In Russian. Leibin, Valery Moiseevich. Psychoanalysis and the Philosophy of Neo-Freudism. Moscow: Politizdat, 1977. All images are for identification of editions only. Several books of the same edition may be available. Please feel free to request photos of available books.SKU7679841.
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Sprache: Englisch
Verlag: Springer International Publishing, 2016
ISBN 10: 3031009045 ISBN 13: 9783031009044
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the .