Name
Title | ||
|---|---|---|
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we propose a combination of a new pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, and replace the complex convolutional operation by a low-cost multiplexer. We perform experiments on the CIFAR10, CIFAR100 and SVHN and show that the proposed method achieves almost state-of-the-art accuracy, while drastically reducing the computational and memory footprints. We also propose an efficient hardware architecture to accelerate CNN operations. The proposed hardware architecture is a pipeline and accommodates multiple layers working at the same time to speed up the inference process. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1812.11337 | 0 | 0.34 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ghouthi Boukli Hacene | 1 | 3 | 2.63 |
| Vincent Gripon | 2 | 210 | 27.16 |
| Matthieu Arzel | 3 | 69 | 15.10 |
| Nicolas Farrugia | 4 | 21 | 4.16 |
| Yoshua Bengio | 5 | 42677 | 3039.83 |