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| Presentation | 2020-01-22 17:45 An FPGA Implementation of Monocular Depth Estimation Youki Sada, Masayuki Shimoda, Shimpei Sato, Hiroki Nakahara (titech)  VLD2019-66 CPSY2019-64 RECONF2019-56 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Among a lot of image recognition applications, Convolutional Neural Network (CNN) has gained high accuracy and increasing interest. It is rapidly required to implement a real-time and energy-efficient depth estimation in embedded systems. Because depth estimation is important to understand the scene and it is required on many applications such as robotics, 3D modeling and driving automation systems. The monocular depth estimation estimates the depth from a single RGB image. And it is paid attention due to the reliability of a monocular RGB camera, low cost and its small requirement of hardware resource. Moreover, there is the possibility to replace an expensive depth sensor such as a LiDAR or a stereo camera into the general RGB camera. We choose the CNN-based monocular depth estimation since CNN schemes are able to realize accurate and dense estimation. However, CNN schemes require a massive amount of multiplications and it makes difficult to implement an accurate system under limited device resources. To handle this, we adopt 8-bit quantization and weight pruning in order to implement an FPGA with high inference speed. Then, our CNN-based estimation is demonstrated on OpenVINO Starter Kit due to real-time requirements and energy-efficiency. Because GPUs consume too much of power and CPUs are too slow due to the numerous operations in the CNN, FPGA system is better performance per power using a custom design for the depth estimation. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Convolutional Neural Network / FPGA / Monocular Depth Estimation / Quantization / Pruning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 373, RECONF2019-56, pp. 73-78, Jan. 2020. | |
| Paper # | RECONF2019-56 | |
| Date of Issue | 2020-01-15 (VLD, CPSY, RECONF) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | VLD2019-66 CPSY2019-64 RECONF2019-56 |
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| Conference Information | |
| Committee | IPSJ-SLDM RECONF VLD CPSY IPSJ-ARC |
| Conference Date | 2020-01-22 - 2020-01-24 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Raiosha, Hiyoshi Campus, Keio University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | FPGA Applications, etc. |
| Paper Information | |
| Registration To | RECONF |
| Conference Code | 2020-01-SLDM-RECONF-VLD-CPSY-ARC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An FPGA Implementation of Monocular Depth Estimation |
| Sub Title (in English) | |
| Keyword(1) | Convolutional Neural Network |
| Keyword(2) | FPGA |
| Keyword(3) | Monocular Depth Estimation |
| Keyword(4) | Quantization |
| Keyword(5) | Pruning |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Youki Sada |
| 1st Author's Affiliation | Tokyo Institute of Technology (titech) |
| 2nd Author's Name | Masayuki Shimoda |
| 2nd Author's Affiliation | Tokyo Institute of Technology (titech) |
| 3rd Author's Name | Shimpei Sato |
| 3rd Author's Affiliation | Tokyo Institute of Technology (titech) |
| 4th Author's Name | Hiroki Nakahara |
| 4th Author's Affiliation | Tokyo Institute of Technology (titech) |
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| Speaker | Author-1 |
| Date Time | 2020-01-22 17:45:00 |
| Presentation Time | 25 minutes |
| Registration for | RECONF |
| Paper # | VLD2019-66, CPSY2019-64, RECONF2019-56 |
| Volume (vol) | vol.119 |
| Number (no) | no.371(VLD), no.372(CPSY), no.373(RECONF) |
| Page | pp.73-78 |
| #Pages | 6 |
| Date of Issue | 2020-01-15 (VLD, CPSY, RECONF) |
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