Name
Abstract | ||
|---|---|---|
To deploy a CNN on resource-constrained edge platforms, channel pruning techniques promise a significant reduction of implementation costs including memory, computation, and energy consumption without special hardware or software libraries. This paper proposes CHaPR, a novel pruning technique to structurally prune the redundant channels in a trained deep Convolutional Neural Network. CHaPR utilizes a proposed subset selection problem formulation for pruning which it solves using pivoted QR factorization. CHaPR also includes an additional pruning technique for ResNet-like architectures which resolves the issue encountered by some existing channel pruning methods that not all the layers can be pruned. Experimental results on VGG-16 and ResNet-50 models show 4.29X and 2.84X reduction, respectively in computation cost while incurring 2.50% top-1 and 1.40% top-5 accuracy losses. Compared to many existing works, CHaPR performs better when considering an Overall Score metric which accounts for both computation and accuracy. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/COINS49042.2020.9191636 | 2020 International Conference on Omni-layer Intelligent Systems (COINS) |
Keywords | DocType | ISBN |
Convolutional Neural Networks,Model Pruning | Conference | 978-1-7281-6372-7 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| boyu zhang | 1 | 71 | 17.54 |
| Azadeh Davoodi | 2 | 362 | 34.99 |
| Yu Hen Hu | 3 | 1398 | 157.52 |