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| Presentation | 2018-12-05 10:20 An FPGA implementation of Tri-state YOLOv2 using Intel OpenCL Youki Sada, Masayuki Shimoda, Shimpei Sato, Hiroki Nakahara (titech)  RECONF2018-35 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Since the convolutional neural network has a high-performance recognition accuracy, it is expected to implement various applications on an embedded vision system. An FPGA can calculate the inference algorithm with low-latency and low power consumption using a specific circuit. In the paper, we propose a tri-state weight, which is a generalization of a low-precision and sparse~(pruning) for CNN weight, to reduce the operation cost and parameters of YOLO. In the first layer, we set a weight ${-1,0,+1}$ as a ternary CNN, while in the other layers, we set a ${-w,0,+w}$ as a sparse weight CNN. We apply an indirect memory access architecture to skip zero part and propose the weight parallel 2D convolutional circuit. It can be applied to the AlexNet based CNN, which has different size kernels. Thus, we design the AlexNet based YOLOv2 to reduce the number of layers toward low-latency computation. In the experiment, the proposed tri-state scheme CNN reduces the 90% of weight parameter. We implement the proposed tri-state weight YOLOv2 on a DE5aNet DDR4 board, which has the Intel Corp. Arria10 GX, by using Intel FPGA SDK for OpenCL. It archived 429.0 frames per second (FPS) on a car and person recognition. Compared with the Intel Corei7 7700, it was 203.3 times faster, and its performance per power efficiency was 190.0 times better. Also, compared with the GeForce GTX 1070 GPU, it was 1.74 times faster, and its power performance efficiency was 2.63 times better. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Intel OpenCL / Object Detection / Tristate YOLOv2 / Convolutional Neural Network / Ternary / Pruning / FPGA / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 340, RECONF2018-35, pp. 7-12, Dec. 2018. | |
| Paper # | RECONF2018-35 | |
| Date of Issue | 2018-11-28 (RECONF) | |
| ISSN | 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 | RECONF2018-35 |
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| Conference Information | |
| Committee | VLD DC CPSY RECONF CPM ICD IE IPSJ-SLDM |
| Conference Date | 2018-12-05 - 2018-12-07 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Satellite Campus Hiroshima |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Design Gaia 2018 -New Field of VLSI Design- |
| Paper Information | |
| Registration To | RECONF |
| Conference Code | 2018-12-VLD-DC-CPSY-RECONF-CPM-ICD-IE-SLDM-EMB-ARC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An FPGA implementation of Tri-state YOLOv2 using Intel OpenCL |
| Sub Title (in English) | |
| Keyword(1) | Intel OpenCL |
| Keyword(2) | Object Detection |
| Keyword(3) | Tristate YOLOv2 |
| Keyword(4) | Convolutional Neural Network |
| Keyword(5) | Ternary |
| Keyword(6) | Pruning |
| Keyword(7) | FPGA |
| 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 | 2018-12-05 10:20:00 |
| Presentation Time | 25 minutes |
| Registration for | RECONF |
| Paper # | RECONF2018-35 |
| Volume (vol) | vol.118 |
| Number (no) | no.340 |
| Page | pp.7-12 |
| #Pages | 6 |
| Date of Issue | 2018-11-28 (RECONF) |
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