Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations

15 October 2021

Papers citing "Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations"

7 / 7 papers shown

Title
Effective Interplay between Sparsity and Quantization: From Theory to Practice Simla Burcu Harma Ayan Chakraborty Elizaveta Kostenok Danila Mishin Dongho Ha ... Martin Jaggi Ming Liu Yunho Oh Suvinay Subramanian Amir Yazdanbakhsh MQ 20 4 0 31 May 2024
An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling Ian Colbert Ken Kreutz-Delgado Srinjoy Das 25 4 0 15 Jul 2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 125 526 0 31 Jan 2021
Pruning and Quantization for Deep Neural Network Acceleration: A Survey Tailin Liang C. Glossner Lei Wang Shaobo Shi Xiaotong Zhang MQ 118 508 0 24 Jan 2021
Universal Deep Neural Network Compression Yoojin Choi Mostafa El-Khamy Jungwon Lee MQ 76 81 0 07 Feb 2018
Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros SSeg 203 7,006 0 21 Nov 2016
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi Jose Caballero Ferenc Huszár J. Totz Andrew P. Aitken Rob Bishop Daniel Rueckert Zehan Wang SupR 177 4,748 0 16 Sep 2016