Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators

9 January 2020

Papers citing "Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators"

3 / 3 papers shown

Title
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments Angie Boggust Venkatesh Sivaraman Yannick Assogba Donghao Ren Dominik Moritz Fred Hohman VLM 50 3 0 06 Aug 2024
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy Lucas Liebenwein Cenk Baykal Brandon Carter David K Gifford Daniela Rus AAML 27 71 0 04 Mar 2021
FlexSA: Flexible Systolic Array Architecture for Efficient Pruned DNN Model Training Sangkug Lym M. Erez 13 25 0 27 Apr 2020