Name
Abstract | ||
|---|---|---|
We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy, but each image evaluation requires millions of floating point operations, making their deployment on smartphones and Internet-scale clusters problematic. The computation is dominated by the convolution operations in the lower layers of the model. We exploit the redundancy present within the convolutional filters to derive approximations that significantly reduce the required computation. Using large state-of-the-art models, we demonstrate speedups of convolutional layers on both CPU and GPU by a factor of 2x, while keeping the accuracy within 1% of the original model. |
Year | Venue | DocType |
|---|---|---|
2014 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014) | Conference |
Volume | ISSN | Citations |
27 | 1049-5258 | 230 |
PageRank | References | Authors |
16.58 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emily Denton | 1 | 378 | 25.96 |
| Wojciech Zaremba | 2 | 2733 | 117.55 |
| J. Bruna | 3 | 1697 | 82.95 |
| Yann LeCun | 4 | 26090 | 3771.21 |
| Robert Fergus | 5 | 11214 | 735.18 |