394 citations · 1.1k across the 21 of their papers we have counts for
Showing cs.CVShow all
3 papers · 1 filter
cs.CV2019★ 9 cited
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
Jiecao Yu, Jongsoo Park, Maxim Naumov
Deep convolutional neural networks (CNNs) are deployed in various applications but demand immense computational requirements. Pruning techniques and Winograd convolution are two ty…
cs.CV2017★ 47 cited
Enabling Sparse Winograd Convolution by Native Pruning
Sheng Li, Jongsoo Park, Ping Tak Peter Tang
Sparse methods and the use of Winograd convolutions are two orthogonal approaches, each of which significantly accelerates convolution computations in modern CNNs. Sparse Winograd…
cs.CV2016★ 133 cited
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Jongsoo Park, Sheng Li, Wei Wen +4
Phenomenally successful in practical inference problems, convolutional neural networks (CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The numbe…