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Condition: As New. Unread book in perfect condition.
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Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 81.52
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Condition: As New. Unread book in perfect condition.
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Condition: New. Softcover reprint of the original 1st ed. 2019 edition NO-PA16APR2015-KAP.
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Embedded Deep Learning : Algorithms, Arc
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New.
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Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 117.35
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New.
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Condition: New.
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Taschenbuch. Condition: Neu. Embedded Deep Learning | Algorithms, Architectures and Circuits for Always-on Neural Network Processing | Bert Moons (u. a.) | Taschenbuch | xvi | Englisch | 2019 | Springer | EAN 9783030075774 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Language: English
Published by Springer International Publishing, Springer International Publishing, 2019
ISBN 10: 303007577X ISBN 13: 9783030075774
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy - applications, algorithms, hardware architectures, and circuits - supported by real silicon prototypes;Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization's implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
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Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 130.13
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Hardcover. Condition: Brand New. 224 pages. 9.25x6.10x0.67 inches. In Stock.
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Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing
Language: English
Published by Springer International Publishing, Springer International Publishing, 2018
ISBN 10: 3319992228 ISBN 13: 9783319992228
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy - applications, algorithms, hardware architectures, and circuits - supported by real silicon prototypes;Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization's implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
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Hardcover. Condition: New. New. book.
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Condition: new. Questo è un articolo print on demand.
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Condition: new. Questo è un articolo print on demand.
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Embedded Deep Learning
Language: English
Published by Springer International Publishing Jan 2019, 2019
ISBN 10: 303007577X ISBN 13: 9783030075774
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy - applications, algorithms, hardware architectures, and circuits - supported by real silicon prototypes;Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization's implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts. 224 pp. Englisch.
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Embedded Deep Learning
Language: English
Published by Springer International Publishing, 2019
ISBN 10: 303007577X ISBN 13: 9783030075774
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devicesDiscusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
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Embedded Deep Learning
Language: English
Published by Springer, Springer Jan 2019, 2019
ISBN 10: 303007577X ISBN 13: 9783030075774
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy ¿ applications, algorithms, hardware architectures, and circuits ¿ supported by real silicon prototypes;Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization¿s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 224 pp. Englisch.
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Embedded Deep Learning
Language: English
Published by Springer International Publishing Nov 2018, 2018
ISBN 10: 3319992228 ISBN 13: 9783319992228
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy - applications, algorithms, hardware architectures, and circuits - supported by real silicon prototypes;Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization's implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts. 224 pp. Englisch.
-
Embedded Deep Learning
Language: English
Published by Springer International Publishing, 2018
ISBN 10: 3319992228 ISBN 13: 9783319992228
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devicesDiscusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy.
-
Embedded Deep Learning
Language: English
Published by Springer, Springer Nov 2018, 2018
ISBN 10: 3319992228 ISBN 13: 9783319992228
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy ¿ applications, algorithms, hardware architectures, and circuits ¿ supported by real silicon prototypes;Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization¿s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 224 pp. Englisch.
