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An OpenCL Framework for Real-time Inference of Next-Generation Convolutional Neural Networks on FPGAs.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	An OpenCL Framework for Real-time Inference of Next-Generation Convolutional Neural Networks on FPGAs./
Author:	Kumawat, Sachin.
Description:	1 online resource (93 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355764413

An OpenCL Framework for Real-time Inference of Next-Generation Convolutional Neural Networks on FPGAs.
Kumawat, Sachin.

An OpenCL Framework for Real-time Inference of Next-Generation Convolutional Neural Networks on FPGAs. - 1 online resource (93 pages)

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

Thesis (M.S.)--University of California, Davis, 2017.

Includes bibliographical references

Modern Convolutional Neural Networks (CNNs) consist of billions of multiplications and additions which require the use of parallel computing units such as GPUs, FPGAs and other DSP processors. Consequently, General Purpose GPU (GPGPU) computing has taken this field by storm. At the same time, there has been an increased interest in FPGA based acceleration of CNN inference.

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

Mode of access: World Wide Web

ISBN: 9780355764413Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

An OpenCL Framework for Real-time Inference of Next-Generation Convolutional Neural Networks on FPGAs.
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245 1 3 $a An OpenCL Framework for Real-time Inference of Next-Generation Convolutional Neural Networks on FPGAs.
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300 $a 1 online resource (93 pages)
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500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Soheil Ghiasi.
502 $a Thesis (M.S.)--University of California, Davis, 2017.
504 $a Includes bibliographical references
520 $a Modern Convolutional Neural Networks (CNNs) consist of billions of multiplications and additions which require the use of parallel computing units such as GPUs, FPGAs and other DSP processors. Consequently, General Purpose GPU (GPGPU) computing has taken this field by storm. At the same time, there has been an increased interest in FPGA based acceleration of CNN inference.
520 $a In this work, we present FICaffe, a framework for FPGA-based Inference with Caffe, which provides a complete automated generation and mapping of CNN accelerators on FPGAs. We target applications with critical latency requirements and design high processing efficiency accelerators for CNNs. The architecture is structured in a highly concurrent OpenCL library, which enables High Level Synthesis tools to effectively exploit data, task and pipeline parallelism. We propose a unified memory model, that drives exploration of optimal design by matching on-chip and off-chip memory bandwidths available on FPGA platforms. We also identify origins of all clock cycle stalls and overheads inherent to CNN acceleration designs and provide a detailed model to accurately predict the runtime latency with less than 4% error against on-board tests. Furthermore, with FICaffe we provide support for cross-network synthesis, such that it is possible to processes a variety of CNNs, with reasonable efficiency, without long re-compilation hours. FICaffe is integrated with the popular deep learning framework Caffe, and is deployable to a wide variety of CNNs. FICaffe's efficacy is shown by mapping to a 28nm Stratix V GXA7 chip, and both network specific and cross-network performance are reported for AlexNet, VGG, SqueezeNet and GoogLeNet. We show a processing efficiency of 95.8% for the widely-reported VGG benchmark, which outperforms prior work. FICaffe also achieves more than 2X speedup on Stratix V GXA7 compared with the best published results on this chip, to the best of our knowledge.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer engineering. $3 569006
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