Name
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) have achieved impressive performance in a wide range of computer vision areas. However, the application on mobile devices remains intractable due to the high computation complexity. In this demo, we propose the Quantized CNN (Q-CNN), an efficient framework for CNN models, to fulfill efficient and accurate image classification on mobile devices. Our Q-CNN framework dramatically accelerates the computation and reduces the storage/memory consumption, so that mobile devices can independently run an ImageNet-scale CNN model. Experiments on the ILSVRC-12 dataset demonstrate 4 ~ 6 × speedup and 15 ~ 20× compression, with merely one percentage drop in the classification accuracy. Based on the Q-CNN framework, even mobile devices can accurately classify images within one second. |
Year | Venue | Field |
|---|---|---|
2016 | AAAI | Convolutional neural network,Computer science,Mobile device,Artificial intelligence,Contextual image classification,Quantization (signal processing),Machine learning,Computation complexity,Computation,Speedup |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaxiang Wu | 1 | 191 | 10.12 |
| qinghao hu | 2 | 163 | 8.86 |
| Cong Leng | 3 | 241 | 13.20 |
| Jian Cheng | 4 | 1327 | 115.72 |