Neuro Inspired Computing Using Resistive (23 results)

xi, 269 p. 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.

Condition: New.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: New. This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology. Softcover reprint of the original 1st ed. 2017.

Paperback. Condition: Brand New. reprint edition. 269 pages. 9.25x6.10x0.64 inches. In Stock.

Taschenbuch. Condition: Neu. Neuro-inspired Computing Using Resistive Synaptic Devices | Shimeng Yu | Taschenbuch | xi | Englisch | 2018 | Springer | EAN 9783319853680 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Condition: Neu. Neuware -This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 284 pp. Englisch.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.

Paperback. Condition: New. This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology. Softcover reprint of the original 1st ed. 2017.

Hardcover. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: New. pp. 280.

Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology. 284 pp. Englisch.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology. 284 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-levelIncludes detailed discussion of the peripheral circuits and array architecture design of .

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-levelIncludes detailed discussion of the peripheral circuits and array architecture design of .

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 284 pp. Englisch.

Condition: New. Print on Demand pp. 280.

Condition: New. PRINT ON DEMAND pp. 280.