Backprop with Approximate Activations for Memory-efficient Network Training

23 January 2019

Papers citing "Backprop with Approximate Activations for Memory-efficient Network Training"

8 / 8 papers shown

Title
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation Yuchen Yang Yingdong Shi Cheems Wang Xiantong Zhen Yuxuan Shi Jun Xu 40 1 0 24 Jun 2024
Do We Really Need a Large Number of Visual Prompts? Youngeun Kim Yuhang Li Abhishek Moitra Ruokai Yin Priyadarshini Panda VLM VPVLM 46 5 0 26 May 2023
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees Jue Wang Binhang Yuan Luka Rimanic Yongjun He Tri Dao Beidi Chen Christopher Ré Ce Zhang AI4CE 31 11 0 02 Jun 2022
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training Joya Chen Kai Xu Yuhui Wang Yifei Cheng Angela Yao 19 7 0 28 Feb 2022
Mesa: A Memory-saving Training Framework for Transformers Zizheng Pan Peng Chen Haoyu He Jing Liu Jianfei Cai Bohan Zhuang 31 20 0 22 Nov 2021
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training Jianfei Chen Lianmin Zheng Z. Yao Dequan Wang Ion Stoica Michael W. Mahoney Joseph E. Gonzalez MQ 27 74 0 29 Apr 2021
Enabling Binary Neural Network Training on the Edge Erwei Wang James J. Davis Daniele Moro Piotr Zielinski Jia Jie Lim C. Coelho S. Chatterjee P. Cheung George A. Constantinides MQ 20 24 0 08 Feb 2021
Faster Neural Network Training with Approximate Tensor Operations Menachem Adelman Kfir Y. Levy Ido Hakimi M. Silberstein 31 26 0 21 May 2018