Hardware reconstruction acceleration method of convolutional neural network-based single image defogging model

doi:10.11772/j.issn.1001-9081.2021081475

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (10): 3184-3190.DOI: 10.11772/j.issn.1001-9081.2021081475

• Multimedia computing and computer simulation • Previous Articles

Hardware reconstruction acceleration method of convolutional neural network-based single image defogging model

Guanjun WANG¹, Chunlian JIAN², Qiang XIANG¹

^1.College of Electronic and Information，Southwest Minzu University，Chengdu Sichuan 610041，China
^2.Chengdu Powerview Science and Technology Company Limited，Chengdu Sichuan 610046，China

Received:2021-08-18 Revised:2021-11-22 Accepted:2021-11-24 Online:2022-01-07 Published:2022-10-10
Contact: Qiang XIANG
About author:WANG Guanjun, born in 1990， M. S. candidate. His research interests include image processing， computer vision， embedded system.
JIAN Chunlian, born in 1988， engineer. Her research interests include image processing， computer vision.
XIANG Qiang, born in 1973， Ph. D. ， associate professor. His research interests include signal and information processing， embedded system.
Supported by:
Key Research and Development of Science and Technology Department of Sichuan Province(22ZDYF3125);Innovative Scientific Research Project for Graduates of Southwest Minzu University(CX2021SZ46)

基于卷积神经网络的单图像去雾模型硬件重构加速方法

王官军¹, 简春莲², 向强¹

^1.西南民族大学电子信息学院，成都 610041
^2.成都动力视讯科技股份有限公司，成都 610046

通讯作者: 向强
作者简介:第一联系人：王官军（1990—），男，四川内江人，硕士研究生，主要研究方向：图像处理、计算机视觉、嵌入式系统
简春莲（1988—），女，四川达州人，工程师，主要研究方向：图像处理、计算机视觉
向强（1973—），男，四川营山人，副教授，博士，主要研究方向：信号与信息处理、嵌入式系统。xiangqiang@swun.edu.cn
基金资助:
四川省科技厅重点研发项目(22ZDYF3125);西南民族大学研究生创新型科研项目(CX2021SZ46)

Abstract

Abstract:

Single image defogging model based on Convolutional Neural Network （CNN） was difficult to deploy on mobile/embedded system and used for real-time video defogging. To solve this problem， a method of hardware reconstruction and acceleration was proposed， based on Zynq System-on-Chip （SoC）. First， a quantization-dequantization algorithm was proposed to perform quantization on two representative defogging models； second， a quantized defogging model was reconstructed and a hardware IP core with Advanced eXtensible Interface 4 （AXI4） was generated， based on video stream memory architecture， hardware/software co-design， pipeline technology and High-Level Synthesis （HLS） tool. Experimental results show that the model parameters can be quantified from float32 to int5（5 bit） under premise of defogging performance， saving about 84.4% of storage space； the highest pixel clock frequency of the generated hardware IP core is 182 Mpixel/s， which can achieve 1080P@60 frame/s video defogging； the hardware IP core processes a single hazy image with the resolution of 640 pixel × 480 pixel only in 2.4 ms， and the on-chip power consumption is only 2.25 W. This hardware IP core with AXI4 is also convenient for cross-platform migration and deployment， which can expand application scope of CNN-based single image defogging model.

Key words: defogging, Video Direct Memory Access (VDMA), model quantization, model reconstruction, hardware IP core, High-Level Synthesis (HLS)

摘要：

针对基于卷积神经网络（CNN）的单图像去雾模型在移动/嵌入式端部署难，不易用做实时视频去雾的问题，提出一种基于Zynq片上系统（SoC）的去雾模型硬件重构加速方法。首先，提出量化?反量化算法，对两个代表去雾模型进行量化；其次，基于视频流存储器架构和软硬件协同、流水线等技术以及高级综合（HLS）工具，对量化后的去雾模型硬件重构并生成具有高性能扩展总线接口（AXI4）的硬件IP核。实验结果表明，在保证去雾效果的前提下，可以实现模型参数从float32到int5（5 bit）的量化，从而节省约84.4%的存储空间；所生成硬件IP核的最高像素时钟频率为182 Mpixel/s，能够实现1080P@60 frame/s的视频去雾；单帧640×480的雾图去雾仅需2.4 ms，而片上功耗仅为2.25 W。这种生成带有标准总线接口的硬件IP核也便于跨平台移植和部署，从而可以扩大这类去雾模型的应用范围。

关键词: 去雾, 视频直接存储器访问, 模型量化, 模型重构, 硬件IP核, 高级综合

CLC Number:

TP391.4

Guanjun WANG, Chunlian JIAN, Qiang XIANG. Hardware reconstruction acceleration method of convolutional neural network-based single image defogging model[J]. Journal of Computer Applications, 2022, 42(10): 3184-3190.

王官军, 简春莲, 向强. 基于卷积神经网络的单图像去雾模型硬件重构加速方法[J]. 《计算机应用》唯一官方网站, 2022, 42(10): 3184-3190.

Figures/Tables 12

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数据类型	均值		标准差		平均梯度
数据类型	DehazeNet	AOD-Net	DehazeNet	AOD-Net	DehazeNet	AOD-Net
float32	111.75	103.72	63.56	59.28	11.02	10.60
8 bit	0.55	-0.02	-0.01	0.00	0.05	0.00
7 bit	-1.80	-0.02	1.03	0.11	0.15	0.00
6 bit	2.25	-1.88	-0.17	0.58	-0.18	0.07
5 bit	-0.04	1.25	2.37	-0.76	-0.30	-0.02
4 bit	-9.41	8.85	8.08	-3.65	-0.43	-0.58
3 bit	9.11	-13.96	-11.06	3.22	0.05	0.56
2 bit	-27.57	-33.67	21.51	5.43	-5.88	-0.39
数据类型	MSE		PSNR/dB		SSIM
数据类型	DehazeNet	AOD-Net	DehazeNet	AOD-Net	DehazeNet	AOD-Net
float32	—	—	—	—	—	—
8 bit	0.96	0.32	24.71	27.81	0.99	0.99
7 bit	3.21	0.55	21.88	26.13	0.98	0.99
6 bit	5.02	2.46	21.23	22.36	0.98	0.99
5 bit	12.44	2.06	18.92	23.07	0.95	0.98
4 bit	127.68	35.86	13.98	16.53	0.80	0.94
3 bit	307.04	81.13	12.05	14.72	0.83	0.88
2 bit	1 454.90	483.89	8.78	11.00	0.42	0.56

数据类型	均值		标准差		平均梯度
数据类型	DehazeNet	AOD-Net	DehazeNet	AOD-Net	DehazeNet	AOD-Net
float32	111.75	103.72	63.56	59.28	11.02	10.60
8 bit	0.55	-0.02	-0.01	0.00	0.05	0.00
7 bit	-1.80	-0.02	1.03	0.11	0.15	0.00
6 bit	2.25	-1.88	-0.17	0.58	-0.18	0.07
5 bit	-0.04	1.25	2.37	-0.76	-0.30	-0.02
4 bit	-9.41	8.85	8.08	-3.65	-0.43	-0.58
3 bit	9.11	-13.96	-11.06	3.22	0.05	0.56
2 bit	-27.57	-33.67	21.51	5.43	-5.88	-0.39
数据类型	MSE		PSNR/dB		SSIM
数据类型	DehazeNet	AOD-Net	DehazeNet	AOD-Net	DehazeNet	AOD-Net
float32	—	—	—	—	—	—
8 bit	0.96	0.32	24.71	27.81	0.99	0.99
7 bit	3.21	0.55	21.88	26.13	0.98	0.99
6 bit	5.02	2.46	21.23	22.36	0.98	0.99
5 bit	12.44	2.06	18.92	23.07	0.95	0.98
4 bit	127.68	35.86	13.98	16.53	0.80	0.94
3 bit	307.04	81.13	12.05	14.72	0.83	0.88
2 bit	1 454.90	483.89	8.78	11.00	0.42	0.56

硬件资源名	实际消耗数	资源总数/10²	资源消耗率/%
BRAM_18K	114	10	11
DSP48E	540	9	60
FF	136 044	3 438	39
LUT	115 343	1 719	67

硬件资源名	实际消耗数	资源总数/10²	资源消耗率/%
BRAM_18K	114	10	11
DSP48E	540	9	60
FF	136 044	3 438	39
LUT	115 343	1 719	67

硬件资源名	实际消耗数	资源总数/10²	资源消耗率/%
BRAM_18K	114	10	11
DSP48E	551	9	61
FF	124 686	3 438	36
LUT	122 049	1 719	71

Hardware reconstruction acceleration method of convolutional neural network-based single image defogging model

基于卷积神经网络的单图像去雾模型硬件重构加速方法

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Figures/Tables 12

References 24

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Metrics

去雾模型	平均时间	去雾模型	平均时间
AOD-Net^［8］	0.650 0	GCA-Net^［11］	0.230 0
Dehaze-Net^［6］	5.090 0	文献［7］模型	0.048 9
FAOD-Net^［9］	0.340 0	硬件IP核的AOD-Net模型	0.002 4
FAMED-Net^［10］	0.890 0

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