Name
Abstract | ||
|---|---|---|
In recent years, the use of image recognition technology in edge devices has been increasing. To achieve low-power and low-latency inference of convolutional neural networks (CNNs) in edge devices, methods that reduce the number of operations, such as pruning, have been actively researched. However, even after applying these existing methods, we still need to calculate many multiply-accumulate (MA... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CYBCONF51991.2021.9464152 | 2021 5th IEEE International Conference on Cybernetics (CYBCONF) |
Keywords | DocType | ISBN |
Radio frequency,Quantization (signal),Image edge detection,Neural networks,Predictive models,Approximation methods,Reliability | Conference | 978-1-6654-0320-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sho Yatabe | 1 | 0 | 0.34 |
| Sora Isobe | 2 | 0 | 0.34 |
| Yoichi Tomioka | 3 | 7 | 5.54 |
| Hiroshi Saito | 4 | 0 | 1.35 |
| Yukihide Kohira | 5 | 0 | 0.68 |
| Qiangfu Zhao | 6 | 214 | 62.36 |