Name
Title | ||
|---|---|---|
Area-efficient Binary and Ternary CNN Accelerator using Random-forest-based Approximation |
Abstract | ||
|---|---|---|
In recent years, the demand for faster inference of convolutional neural networks with a smaller and low-power accelerator is increasing to realize low-latency control of robots and reduce network load. In this paper, we propose a random-forest-based approximation layer unit (RFA-LU) for binary and ternary CNNs to realize faster inference. This unit introduces a novel technique predicting output f... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CANDAR53791.2021.00023 | 2021 Ninth International Symposium on Computing and Networking (CANDAR) |
Keywords | DocType | ISBN |
FPGA,Random forest,CNN,Approximation computing | Conference | 978-1-6654-4246-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaisei Kimura | 1 | 0 | 0.34 |
| Sho Yatabe | 2 | 0 | 0.34 |
| Sora Isobe | 3 | 0 | 0.34 |
| Yoichi Tomioka | 4 | 7 | 5.54 |
| Hiroshi Saito | 5 | 0 | 1.35 |
| Yukihide Kohira | 6 | 0 | 0.34 |
| Qiangfu Zhao | 7 | 214 | 62.36 |