Data Orchestration in Deep Learning Accelerators

Language: English

Published by Springer, 2020

3031006399 / 9783031006395

Series: Book 6 of 7 - Synthesis Lectures on Computer Architecture

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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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 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.…

Seller Inventory # 9783031006395

Title
Data Orchestration in Deep Learning Accelerators
Author
Hyoukjun Kwon
Publisher
Springer
Publication year
2020
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3031006399
ISBN 13
9783031006395
Item weight
327 grams
Dimensions
235x191x10 mm
Series
Book 6 of 7: Synthesis Lectures on Computer Architecture

AHA-BUCH GmbH

Einbeck, Germany

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AbeBooks seller since August 14, 2006

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