CEST: Computation-Efficient N:M Sparse Training for Deep Neural Networks
Author:
Affiliation:
1. Nanjing University,ICAIS Lab
2. Eindhoven University of Technology
3. CUHK,CUHK-Sensetime Joint Lab
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10136870/10136706/10137121.pdf?arnumber=10137121
Reference9 articles.
1. FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10
2. EF-Train: Enable Efficient On-device CNN Training on FPGA through Data Reshaping for Online Adaptation or Personalization
3. An FPGA-based processor for training convolutional neural networks
4. An Algorithm–Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers
5. Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:m Transposable Masks;hubara;NeurIPS,2021
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-02
2. Hardware Accelerator Design for Sparse DNN Inference and Training: A Tutorial;IEEE Transactions on Circuits and Systems II: Express Briefs;2023
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