Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
2006th ed. 16 x 23 cm. 266 pages. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
Language: English
Published by The Institution of Engineering and Technology, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New.
Language: English
Published by Institution of Engineering and Technology, GB, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.
Hardback. Condition: New. This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.
Language: English
Published by The Institution of Engineering and Technology, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: California Books, Miami, FL, U.S.A.
Condition: New.
Language: English
Published by The Institution of Engineering and Technology, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Language: English
Published by The Institution of Engineering and Technology, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 111.19
Quantity: Over 20 available
Add to basketCondition: New.
Language: English
Published by The Institution of Engineering and Technology, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 111.96
Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
Language: English
Published by The Institution of Engineering and Technology, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 122.49
Quantity: Over 20 available
Add to basketCondition: New. In English.
Language: English
Published by Institution of Engineering and Technology, GB, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
£ 153.13
Quantity: Over 20 available
Add to basketHardback. Condition: New. This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.
Language: English
Published by Institution of Engineering and Technology, GB, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.
Hardback. Condition: New. This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.
£ 137.84
Quantity: Over 20 available
Add to basketCondition: New. In.
Condition: New.
Condition: New.
Language: English
Published by Inst of Engineering & Technology, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 300 pages. 9.21x6.14x0.75 inches. In Stock.
Condition: New.
Condition: New. pp. 426.
Condition: As New. Unread book in perfect condition.
Condition: New. pp. 426 219 Illus. (97 Col.).
Language: English
Published by INSTITUTION OF ENGINEERING & T, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: moluna, Greven, Germany
Gebunden. Condition: New. Inhaltsverzeichnisrnrnn Part I: Deep learning and neural networks: concepts and modelsn Chapter 1: An introduction to artificial neural networksn Chapter 2: Hardware acceleration for recurrent neural networksn C.
Taschenbuch. Condition: Neu. Routing Algorithms in Networks-on-Chip | Masoud Daneshtalab (u. a.) | Taschenbuch | Paperback | xiv | Englisch | 2016 | Springer US | EAN 9781493955114 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Condition: As New. Unread book in perfect condition.
Language: English
Published by Springer New York, Springer US, 2016
ISBN 10: 149395511X ISBN 13: 9781493955114
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a single-source reference to routing algorithms for Networks-on-Chip (NoCs), as well as in-depth discussions of advanced solutions applied to current and next generation, many core NoC-based Systems-on-Chip (SoCs). After a basic introduction to the NoC design paradigm and architectures, routing algorithms for NoC architectures are presented and discussed at all abstraction levels, from the algorithmic level to actual implementation. Coverage emphasizes the role played by the routing algorithm and is organized around key problems affecting current and next generation, many-core SoCs. A selection of routing algorithms is included, specifically designed to address key issues faced by designers in the ultra-deep sub-micron (UDSM) era, including performance improvement, power, energy, and thermal issues, fault tolerance and reliability.
Language: English
Published by Springer New York, Springer New York, 2013
ISBN 10: 1461482739 ISBN 13: 9781461482734
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a single-source reference to routing algorithms for Networks-on-Chip (NoCs), as well as in-depth discussions of advanced solutions applied to current and next generation, many core NoC-based Systems-on-Chip (SoCs). After a basic introduction to the NoC design paradigm and architectures, routing algorithms for NoC architectures are presented and discussed at all abstraction levels, from the algorithmic level to actual implementation. Coverage emphasizes the role played by the routing algorithm and is organized around key problems affecting current and next generation, many-core SoCs. A selection of routing algorithms is included, specifically designed to address key issues faced by designers in the ultra-deep sub-micron (UDSM) era, including performance improvement, power, energy, and thermal issues, fault tolerance and reliability.
Language: English
Published by Institution of Engineering and Technology, GB, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: Rarewaves.com UK, London, United Kingdom
£ 139.42
Quantity: Over 20 available
Add to basketHardback. Condition: New. This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.
Hardcover. Condition: Brand New. 410 pages. 9.25x6.25x1.25 inches. In Stock.
Seller: UK BOOKS STORE, London, LONDO, United Kingdom
Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Language: English
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642068146 ISBN 13: 9783642068140
Seller: moluna, Greven, Germany
Condition: New. Heterocyclic chemistry is the biggest branch of chemistry covering two-third of the chemical literature. Research papers in heterocyclic chemistry are published in almost all the journals of chemistry and organic chemistry in addition to five journals de.
Condition: As New. Unread book in perfect condition.
Language: English
Published by Institution Of Engineering & Technology Apr 2020, 2020
ISBN 10: 1785617680 ISBN 13: 9781785617683
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. Neuware - This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a renaissance in interest in neural networks, with applications including image and speech processing, data analytics, robotics, healthcare monitoring, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for embedded and mobile computing platforms with limited computational/storage resources and a tight power budget. Even for cloud-scale systems it is critical to select the right hardware configuration based on the neural network complexity and system constraints in order to increase power- and performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to applications, for researchers, postgraduate students and engineers who work on learning-based services and hardware platforms.