大葉大學圖書館 |

Data orchestration in deep learning accelerators[electronic resource] /

纪录类型:	书目-电子资源 : Monograph/item
[NT 15000414] null:	006.3
[NT 47271] Title/Author:	Data orchestration in deep learning accelerators/ Tushar Krishna, Hyoukjun Kwon, Angshuman Parashar, Michael Pellauer, Ananda Samajdar.
作者:	Krishna, Tushar,
[NT 51406] other author:	Kwon, Hyoukjun,
面页册数:	1 online resource (166 p.)
标题:	Neural networks (Computer science)
标题:	Machine learning.
标题:	Data flow computing.
ISBN:	9781681738697
ISBN:	9781681738703
ISBN:	9781681738710
[NT 15000227] null:	Includes bibliographical references (pages 131-143).
[NT 15000229] null:	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
电子资源:	https://portal.igpublish.com/iglibrary/search/MCPB0006576.html

Data orchestration in deep learning accelerators[electronic resource] /
Krishna, Tushar,

Data orchestration in deep learning accelerators[electronic resource] /Tushar Krishna, Hyoukjun Kwon, Angshuman Parashar, Michael Pellauer, Ananda Samajdar. - 1 online resource (166 p.) - Synthesis lectures on computer architecture ;52. - Synthesis lectures on computer architecture ;52..

Includes bibliographical references (pages 131-143).

Access restricted to authorized users and institutions.

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

Mode of access: World Wide Web.

ISBN: 9781681738697Subjects--Topical Terms:

386157
Neural networks (Computer science)
Index Terms--Genre/Form:

336502
Electronic books.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Data orchestration in deep learning accelerators[electronic resource] /
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