Name
Title | ||
|---|---|---|
Real-Time Object Detection and Semantic Segmentation Hardware System with Deep Learning Networks |
Abstract | ||
|---|---|---|
Advanced Driver Assistance Systems (ADAS) help the driver in the driving process by detecting objects, doing basic classification, implementing safety guards and so on. Convolution Neural Networks (CNN) has been proved to be an essential to support ADAS. We designed an architecture named Aristotle to execute neural networks for both object detection and semantic segmentation on FPGA. DNNDK (Deep Learning Development Toolkit), a full-stack software tool, with tens of compilation optimization techniques is proposed to improve the energy efficiency and make it easy to develop. The Aristotle architecture is implemented on Xilinx ZU9 FPGA, and two networks are deployed on it to execute object detection and semantic segmentation, respectively. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FPT.2018.00081 | 2018 International Conference on Field-Programmable Technology (FPT) |
Keywords | Field | DocType |
ADAS,deep learning,CNN,object detection,semantic segmentation | Object detection,Convolution,Computer science,Segmentation,Efficient energy use,Advanced driver assistance systems,Field-programmable gate array,Artificial intelligence,Deep learning,Computer hardware,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-7281-0215-3 | 1 | 0.43 |
References | Authors | |
0 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaoxia Fang | 1 | 2 | 1.85 |
| Lu Tian | 2 | 14 | 7.53 |
| Junbin Wang | 3 | 1 | 0.43 |
| Shuang Liang | 4 | 60 | 12.33 |
| Dongliang Xie | 5 | 251 | 21.85 |
| Zhongmin Chen | 6 | 1 | 0.43 |
| Lingzhi Sui | 7 | 78 | 5.86 |
| Qian Yu | 8 | 272 | 23.02 |
| Xiaoming Sun | 9 | 3 | 1.81 |
| Yi Shan | 10 | 253 | 15.77 |
| Yu Wang | 11 | 23 | 3.71 |