Data Orchestration Deep Learning by Krishna Tushar (14 results)
Data Orchestration in Deep Learning Accelerators
Krishna, Tushar; Kwon, Hyoukjun; Parashar, Angshuman; Pellauer, Michael; Samajdar, Ananda
Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
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Data Orchestration in Deep Learning Accelerators (Synthesis Lectures on Computer Architecture)
Krishna, Tushar; Kwon, Hyoukjun; Parashar, Angshuman; Pellauer, Michael; Samajdar, Ananda
Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
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Language: English
Published by Springer 2020-08, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
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Data Orchestration in Deep Learning Accelerators (Synthesis Lectures on Computer Architecture)
Krishna, Tushar; Kwon, Hyoukjun; Parashar, Angshuman; Pellauer, Michael; Samajdar, Ananda
Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
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Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
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Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
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Language: English
Published by Springer International Publishing, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neu…ral Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.
Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
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Taschenbuch. Condition: Neu. Data Orchestration in Deep Learning Accelerators | Tushar Krishna (u. a.) | Taschenbuch | Synthesis Lectures on Computer Architecture | xvii | Englisch | 2020 | Springer | EAN 9783031006395 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]har…tmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
- Print on Demand
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Language: English
Published by Springer International Publishing Aug 2020, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
- Print on Demand
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of… custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference. 168 pp. Englisch.
Data Orchestration in Deep Learning Accelerators (Synthesis Lectures on Computer Architecture)
Krishna, Tushar; Kwon, Hyoukjun; Parashar, Angshuman; Pellauer, Michael; Samajdar, Ananda
Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
- Print on Demand
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Data Orchestration in Deep Learning Accelerators (Synthesis Lectures on Computer Architecture)
Krishna, Tushar; Kwon, Hyoukjun; Parashar, Angshuman; Pellauer, Michael; Samajdar, Ananda
Language: English
Published by Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
- Print on Demand
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Data Orchestration in Deep Learning Accelerators
Krishna, Tushar|Kwon, Hyoukjun|Parashar, Angshuman|Pellauer, Michael|Samajdar, Ananda
Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
- Print on Demand
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore s Law, coupled with the increasing growth in deep learning and other AI applications has led to t…he emergence of custom Deep Neural Netwo.
Language: English
Published by Springer, Springer Aug 2020, 2020
Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of cus…tom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 168 pp. Englisch.
