SparseNN: An Energy-Efficient Neural Network Accelerator Exploiting Input and Output Sparsity

3 November 2017

Papers citing "SparseNN: An Energy-Efficient Neural Network Accelerator Exploiting Input and Output Sparsity"

8 / 8 papers shown

Title
Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment Indrajeet Kumar Sinha Shekhar Verma Krishna Pratap Singh FedML 230 0 0 02 Sep 2023
LayerPipe: Accelerating Deep Neural Network Training by Intra-Layer and Inter-Layer Gradient Pipelining and Multiprocessor Scheduling Nanda K. Unnikrishnan Keshab K. Parhi AI4CE 78 10 0 14 Aug 2021
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights Shail Dave Riyadh Baghdadi Tony Nowatzki Sasikanth Avancha Aviral Shrivastava Baoxin Li 267 98 0 02 Jul 2020
Computation on Sparse Neural Networks: an Inspiration for Future Hardware Fei Sun Minghai Qin Tianyun Zhang Liu Liu Yen-kuang Chen Yuan Xie 284 7 0 24 Apr 2020
Non-Blocking Simultaneous Multithreading: Embracing the Resiliency of Deep Neural NetworksMicro (MICRO), 2020 Gil Shomron U. Weiser 156 16 0 17 Apr 2020
Software-defined Design Space Exploration for an Efficient DNN Accelerator ArchitectureIEEE transactions on computers (IEEE Trans. Comput.), 2019 Y. Yu Yingmin Li Shuai Che N. Jha Weifeng Zhang 137 22 0 18 Mar 2019
Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant D. Loroch Franz-Josef Pfreundt Norbert Wehn J. Keuper 94 5 0 27 Aug 2018
Full deep neural network training on a pruned weight budget Maximilian Golub G. Lemieux Mieszko Lis 210 30 0 11 Jun 2018