FPGA‐accelerated deep convolutional neural networks for high throughput and energy efficiency

Summary Recent breakthroughs in the deep convolutional neural networks (CNNs) have led to great improvements in the accuracy of both vision and auditory systems. Characterized by their deep structures and large numbers of parameters, deep CNNs challenge the computational performance of today. Hardwa...

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Bibliographic Details
Published in:Concurrency and computation 2017-10, Vol.29 (20), p.n/a
Main Authors: Qiao, Yuran, Shen, Junzhong, Xiao, Tao, Yang, Qianming, Wen, Mei, Zhang, Chunyuan
Format: Article
Language:English
Subjects:
Accelerator
Artificial neural networks
Caffe
CNN
Computer memory
Computing time
Concurrency
Customization
Energy conversion efficiency
Energy efficiency
Field programmable gate arrays
FPGA
Image classification
Matrix Multiplier
Neural networks
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