A Novel Memory-Efficient Deep Learning Training Framework via
Error-Bounded Lossy Compression

A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

18 November 2020

Dingwen Tao

Papers citing "A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression"

6 / 6 papers shown

Title
Understanding The Effectiveness of Lossy Compression in Machine Learning Training Sets Robert Underwood Jon C. Calhoun Sheng Di Franck Cappello 20 2 0 23 Mar 2024
Federated Learning for Energy Constrained IoT devices: A systematic mapping study Rachid El Mokadem Yann Ben Maissa Zineb El Akkaoui 18 8 0 09 Jan 2023
Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5 Sian Jin Dingwen Tao Houjun Tang Sheng Di S. Byna Z. Lukić Franck Cappello 28 18 0 29 Jun 2022
GACT: Activation Compressed Training for Generic Network Architectures Xiaoxuan Liu Lianmin Zheng Dequan Wang Yukuo Cen Weize Chen ... Zhiyuan Liu Jie Tang Joey Gonzalez Michael W. Mahoney Alvin Cheung VLM GNN MQ 17 30 0 22 Jun 2022
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 16 11 0 02 Jun 2022
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression Sian Jin Chengming Zhang Xintong Jiang Yunhe Feng Hui Guan Guanpeng Li S. Song Dingwen Tao 23 23 0 18 Nov 2021