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Winograd Convolution for Deep Neural Networks: Efficient Point Selection

Winograd Convolution for Deep Neural Networks: Efficient Point Selection

25 January 2022
Syed Asad Alam
Andrew Anderson
B. Barabasz
David Gregg
ArXivPDFHTML

Papers citing "Winograd Convolution for Deep Neural Networks: Efficient Point Selection"

12 / 12 papers shown
Title
Searching for Winograd-aware Quantized Networks
Searching for Winograd-aware Quantized Networks
Javier Fernandez-Marques
P. Whatmough
Andrew Mundy
Matthew Mattina
MQ
37
40
0
25 Feb 2020
DWM: A Decomposable Winograd Method for Convolution Acceleration
DWM: A Decomposable Winograd Method for Convolution Acceleration
Di Huang
Xishan Zhang
Rui Zhang
Tian Zhi
Deyuan He
...
Qi Guo
Zidong Du
Shaoli Liu
Tianshi Chen
Yunji Chen
14
26
0
03 Feb 2020
Winograd Convolution for DNNs: Beyond linear polynomials
Winograd Convolution for DNNs: Beyond linear polynomials
B. Barabasz
David Gregg
20
14
0
13 May 2019
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on
  Widely Used Mobile CPUs
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs
Partha P. Maji
Andrew Mundy
Ganesh S. Dasika
Jesse G. Beu
Matthew Mattina
Robert D. Mullins
36
26
0
04 Mar 2019
Error Analysis and Improving the Accuracy of Winograd Convolution for
  Deep Neural Networks
Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks
B. Barabasz
Andrew Anderson
Kirk M. Soodhalter
David Gregg
23
24
0
29 Mar 2018
FFT-Based Deep Learning Deployment in Embedded Systems
FFT-Based Deep Learning Deployment in Embedded Systems
Sheng Lin
Ning Liu
M. Nazemi
Hongjia Li
Caiwen Ding
Yanzhi Wang
Massoud Pedram
52
52
0
13 Dec 2017
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB
  model size
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
F. Iandola
Song Han
Matthew W. Moskewicz
Khalid Ashraf
W. Dally
Kurt Keutzer
110
7,448
0
24 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
1.3K
192,638
0
10 Dec 2015
Fast Algorithms for Convolutional Neural Networks
Fast Algorithms for Convolutional Neural Networks
Andrew Lavin
Scott Gray
43
875
0
30 Sep 2015
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
297
43,511
0
17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
891
99,991
0
04 Sep 2014
Caffe: Convolutional Architecture for Fast Feature Embedding
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia
Evan Shelhamer
Jeff Donahue
Sergey Karayev
Jonathan Long
Ross B. Girshick
S. Guadarrama
Trevor Darrell
VLM
BDL
3DV
183
14,703
0
20 Jun 2014
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