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Ajouter au panierTaschenbuch. Etat : 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.
Langue: anglais
Edité par Springer International Publishing, Springer International Publishing, 2019
ISBN 10 : 303007577X ISBN 13 : 9783030075774
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Ajouter au panierTaschenbuch. Etat : 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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Ajouter au panierPaperback. Etat : New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Ajouter au panierHardcover. Etat : Brand New. 224 pages. 9.25x6.10x0.67 inches. In Stock.
Langue: anglais
Edité par Springer International Publishing, Springer International Publishing, 2018
ISBN 10 : 3319992228 ISBN 13 : 9783319992228
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierBuch. Etat : 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.
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Ajouter au panierHardcover. Etat : New. New. book.
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Ajouter au panierEtat : new. Questo è un articolo print on demand.
Vendeur : Brook Bookstore On Demand, Napoli, NA, Italie
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Ajouter au panierEtat : new. Questo è un articolo print on demand.
Langue: anglais
Edité par Springer International Publishing Jan 2019, 2019
ISBN 10 : 303007577X ISBN 13 : 9783030075774
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 96,29
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Ajouter au panierTaschenbuch. Etat : 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.
Langue: anglais
Edité par Springer International Publishing, 2019
ISBN 10 : 303007577X ISBN 13 : 9783030075774
Vendeur : moluna, Greven, Allemagne
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Ajouter au panierEtat : 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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Ajouter au panierEtat : New. PRINT ON DEMAND.
Langue: anglais
Edité par Springer, Springer Jan 2019, 2019
ISBN 10 : 303007577X ISBN 13 : 9783030075774
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 96,29
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Ajouter au panierTaschenbuch. Etat : 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.
Langue: anglais
Edité par Springer International Publishing Nov 2018, 2018
ISBN 10 : 3319992228 ISBN 13 : 9783319992228
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 139,09
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Ajouter au panierBuch. Etat : 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.
Langue: anglais
Edité par Springer International Publishing, 2018
ISBN 10 : 3319992228 ISBN 13 : 9783319992228
Vendeur : moluna, Greven, Allemagne
EUR 115,65
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Ajouter au panierEtat : 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.
Langue: anglais
Edité par Springer, Springer Nov 2018, 2018
ISBN 10 : 3319992228 ISBN 13 : 9783319992228
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 139,09
Quantité disponible : 1 disponible(s)
Ajouter au panierBuch. Etat : 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.