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Kernel Mechanisms for Efficient GPU ...

ProQuest Information and Learning Co.

Kernel Mechanisms for Efficient GPU Accelerated Deep Neural Network Inference on Embedded Devices.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Kernel Mechanisms for Efficient GPU Accelerated Deep Neural Network Inference on Embedded Devices./
作者:	Nigam, Hemant.
面頁冊數:	1 online resource (87 pages)
附註:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355850222

Kernel Mechanisms for Efficient GPU Accelerated Deep Neural Network Inference on Embedded Devices.
Nigam, Hemant.

Kernel Mechanisms for Efficient GPU Accelerated Deep Neural Network Inference on Embedded Devices. - 1 online resource (87 pages)

Source: Masters Abstracts International, Volume: 57-05.

Thesis (Master's)--University of Washington, 2018.

Includes bibliographical references

Embedded platforms with integrated graphics processing units (GPUs) are popular choices, for use-cases, like Autonomous machines, to run the Deep Neural Networks (DNNs) inference workload. However, due to a rapid increase in data volume, DNN inference is becoming even more computationally intensive and memory sensitive, which necessitates a mechanism for improving DNN inference efficiency on existing embedded systems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355850222Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Kernel Mechanisms for Efficient GPU Accelerated Deep Neural Network Inference on Embedded Devices.
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245 1 0 $a Kernel Mechanisms for Efficient GPU Accelerated Deep Neural Network Inference on Embedded Devices.
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300 $a 1 online resource (87 pages)
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500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Michael Stiber.
502 $a Thesis (Master's)--University of Washington, 2018.
504 $a Includes bibliographical references
520 $a Embedded platforms with integrated graphics processing units (GPUs) are popular choices, for use-cases, like Autonomous machines, to run the Deep Neural Networks (DNNs) inference workload. However, due to a rapid increase in data volume, DNN inference is becoming even more computationally intensive and memory sensitive, which necessitates a mechanism for improving DNN inference efficiency on existing embedded systems.
520 $a This Master's thesis investigates the memory sensitivity of DNN inference---specifically, the impact of off-chip memory (DRAM) contention on DNN inference performance. It demonstrates a prototype GPU aware memory isolation mechanism: a locking mechanism in the GPU driver to reduce DRAM contention caused by multicore CPUs, thus improving DNN inference efficiency. Experiments performed on a Jetson TX2 board running the Linux4Tegra OS shows the benefits of our proposed mechanism, with up to 13.5% speedup of a micro-benchmark and up to 41% and 86% speedup of two object detection benchmarks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
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773 0 $t Masters Abstracts International $g 57-05(E).
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