An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers

12 August 2022

Papers citing "An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers"

6 / 6 papers shown

Title
QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving Yujun Lin Haotian Tang Shang Yang Zhekai Zhang Guangxuan Xiao Chuang Gan Song Han 77 74 0 07 May 2024
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models Xudong Lu Qi Liu Yuhui Xu Aojun Zhou Siyuan Huang Bo-Wen Zhang Junchi Yan Hongsheng Li MoE 27 25 0 22 Feb 2024
Bi-directional Masks for Efficient N:M Sparse Training Yu-xin Zhang Yiting Luo Mingbao Lin Yunshan Zhong Jingjing Xie Fei Chao Rongrong Ji 32 15 0 13 Feb 2023
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 300 5,761 0 29 Apr 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 139 684 0 31 Jan 2021
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 294 6,943 0 20 Apr 2018