V-SKP: Vectorized Kernel-Based Structured Kernel Pruning for Accelerating Deep Convolutional Neural Networks-Reference-Cited by-同舟云学术

V-SKP: Vectorized Kernel-Based Structured Kernel Pruning for Accelerating Deep Convolutional Neural Networks

Published:2023 Issue: Volume:11 Page:118547-118557
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Koo Kwanghyun¹,Kim Hyun¹^ORCID

Affiliation:

1. Department of Electrical and Information Engineering, Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, South Korea

Funder

Ministry of Science and ICT (MSIT), South Korea, through the Information Technology Research Center (ITRC) Support Program

Institute for Information and Communications Technology Planning and Evaluation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10292653.pdf?arnumber=10292653

Reference43 articles.

1. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding;han;arXiv 1510 00149 [cs],2015

2. ImageNet Large Scale Visual Recognition Challenge

3. The lottery ticket hypothesis: Finding sparse, trainable neural networks;frankle;arXiv 1803 03635,2018

4. PPT-KP: Pruning Point Training-based Kernel Pruning for Deep Convolutional Neural Networks

5. DSD: Dense-sparse-dense training for deep neural networks;han;arXiv 1607 04381,2016

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