Name
Abstract | ||
|---|---|---|
Deep neural networks have revolutionized the field of machine learning by dramatically improving the state-of-the-art in various domains. The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to fast store and train them. Over the past few decades, researches have explored the prospect of sparse DNNs before, during, and after training by pruning edges from the underlying topology. After the above operation, the generated neural network is known as a sparse neural network. More recent works have demonstrated the remarkable results that certain sparse DNNs can train to the same precision as dense DNNs at lower runtime and storage cost. Although existing methods ease the situation that high demand for computation resources severely hinders the deployment of large-scale DNNs in resource-constrained devices, DNNs can be trained at a faster speed and lower cost. In this work, we propose a Fine-tune Structured Sparsity Learning (FSSL) method to regularize the structures of DNNs and accelerate the training of DNNs. FSSL can: (1) learn a compact structure from large sparse DNN to reduce computation cost; (2) obtain a hardware-friendly to accelerate the DNNs evaluation efficiently. Experimental results of the training time and the compression rate show that superior performance and efficiency than the Matlab example code. These speedups are about twice speedups of non-structured sparsity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/HPEC.2019.8916506 | 2019 IEEE High Performance Extreme Computing Conference (HPEC) |
Keywords | Field | DocType |
sparse neural networks,sparse matrices,graph analysis,GPU Computing | Computer science,Artificial intelligence,Deep neural networks | Conference |
ISSN | ISBN | Citations |
2377-6943 | 978-1-7281-5021-5 | 4 |
PageRank | References | Authors |
0.40 | 5 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianzong Wang | 1 | 4 | 0.40 |
| Zhangcheng Huang | 2 | 4 | 0.40 |
| Lingwei Kong | 3 | 4 | 0.74 |
| Jing Xiao | 4 | 7 | 5.78 |
| Pengyu Wang | 5 | 9 | 3.22 |
| Lu Zhang | 6 | 5 | 1.43 |
| Chao Li | 7 | 344 | 37.85 |