TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference

1 September 2020

Papers citing "TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference"

7 / 7 papers shown

Title
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning Mohammad Hossein Samavatian Saikat Majumdar Kristin Barber R. Teodorescu AAML 28 2 0 31 Jul 2022
Energy awareness in low precision neural networks Nurit Spingarn-Eliezer Ron Banner Elad Hoffer Hilla Ben-Yaacov T. Michaeli 41 0 0 06 Feb 2022
Accelerating DNN Training with Structured Data Gradient Pruning Bradley McDanel Helia Dinh J. Magallanes 19 7 0 01 Feb 2022
BitTrain: Sparse Bitmap Compression for Memory-Efficient Training on the Edge Abdelrahman I. Hosny Marina Neseem Sherief Reda MQ 35 4 0 29 Oct 2021
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration Zhi-Gang Liu P. Whatmough Yuhao Zhu Matthew Mattina MQ 19 75 0 16 Jul 2021
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning Hanrui Wang Zhekai Zhang Song Han 43 380 0 17 Dec 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 304 6,996 0 20 Apr 2018