基于硬件仿真系统的边缘计算人工智能视觉芯片设计验证

徐宣哲; 宁珂; 郑学敏; 赵明心; 徐萌萌; 吴南健; 刘力源

doi:10.11959/j.issn.2096-3750.2022.00250

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基于硬件仿真系统的边缘计算人工智能视觉芯片设计验证

专题：物联终端与芯片 | 更新时间：2024-06-05

- 基于硬件仿真系统的边缘计算人工智能视觉芯片设计验证
- Verification of an artificial intelligence vision chip design for edge computing based on hardware simulation system
- 物联网学报 2022年6卷第1期页码：20-28
- 作者机构：
  
  1. 中国科学院半导体研究所半导体超晶格国家重点实验室，北京 100083
  2. 中国科学院大学材料与光电研究中心，北京 100049
- 作者简介：
  
  [ "徐宣哲（1999− ），男，中国科学院半导体研究所半导体超晶格国家重点实验室硕士生，主要研究方向为低功耗视觉芯片的硬件算法验证等" ]
  [ "宁珂（1994− ），男，中国科学院半导体研究所半导体超晶格国家重点实验室博士生，主要研究方向为低功耗视觉芯片设计等" ]
  [ "郑学敏（1995− ），男，中国科学院半导体研究所半导体超晶格国家重点实验室博士生，主要研究方向为低功耗视觉芯片设计等" ]
  [ "赵明心（1992− ），男，中国科学院半导体研究所半导体超晶格国家重点实验室博士生，主要研究方向为卷积神经网络的量化、稀疏化、加速以及软硬件协同设计等" ]
  [ "徐萌萌（1997− ），女，中国科学院半导体研究所半导体超晶格国家重点实验室博士生，主要研究方向为边缘计算型视觉芯片的网络模型压缩等" ]
  [ "吴南健（1961− ），男，博士，中国科学院半导体研究所半导体超晶格国家重点实验室研究员，主要研究方向为大规模数模混合集成电路和视觉芯片等" ]
  [ "刘力源（1982− ），男，博士，中国科学院半导体研究所半导体超晶格国家重点实验室研究员，主要研究方向为高速图像传感器、太赫兹图像传感器、视觉芯片以及数模混合信号集成电路设计等" ]
- 基金信息：
  
  国家重点研发计划;The National Key Research and Development Program of China(2019YFB2204300);国家自然科学基金资助项目;The National Natural Science Foundation of China(U20A20205);国家自然科学基金资助项目;The National Natural Science Foundation of China(61874107);中国科学院青年创新促进会项目;The Program of Youth Innovation Promotion Association, Chinese Academy of Sciences(2021109)
- DOI：10.11959/j.issn.2096-3750.2022.00250
  中图分类号： TN47
- 纸质出版日期：2022-03-30，
  
  网络出版日期：2022-03，
- 稿件说明：
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徐宣哲, 宁珂, 郑学敏, 等. 基于硬件仿真系统的边缘计算人工智能视觉芯片设计验证[J]. 物联网学报, 2022,6(1):20-28.

XUANZHE XU, KE NING, XUEMIN ZHENG, et al. Verification of an artificial intelligence vision chip design for edge computing based on hardware simulation system. [J]. Chinese journal on internet of things, 2022, 6(1): 20-28.
徐宣哲, 宁珂, 郑学敏, 等. 基于硬件仿真系统的边缘计算人工智能视觉芯片设计验证[J]. 物联网学报, 2022,6(1):20-28. DOI： 10.11959/j.issn.2096-3750.2022.00250.

XUANZHE XU, KE NING, XUEMIN ZHENG, et al. Verification of an artificial intelligence vision chip design for edge computing based on hardware simulation system. [J]. Chinese journal on internet of things, 2022, 6(1): 20-28. DOI： 10.11959/j.issn.2096-3750.2022.00250.

摘要

基于卷积神经网络（CNN

convolutional neural network）的视觉深度学习算法的兴起推动了人工智能视觉芯片设计研究的快速发展，而芯片的设计验证工作是人工智能视觉芯片研发的瓶颈。介绍了一种基于硬件仿真系统的人工智能视觉芯片软硬件验证方法，以边缘计算人工智能视觉芯片设计为例，在硬件仿真系统ZeBu上完成了芯片运行的典型深度学习网络MobileNet的仿真验证工作。结果表明，在硬件芯片架构上实现的网络模型在保证精确度的同时，在200 MHz频率时钟下单帧检测时间只需要18.51 ms，与软件平台仿真相比，仿真速度提高了7倍。

Abstract

The rise of visual deep learning algorithms based on convolutional neural network (CNN) has promoted the rapid development of the artificial intelligence (AI) vision chip design research.The step of chip verification is a bottleneck in the development of AI vision chips.A software and hardware verification method for AI vision chip design based on hardware simulation system was introduced.Taking AI vision chip design for edge computing as an example

the chip was run on the hardware simulation system (ZeBu) and the simulation verification work of typical deep learning network MobileNet was completed.The results show that the network model implemented on the hardware chip architecture keeps accuracy while the detection time of a single frame is only 18.51 ms under a 200 MHz clock frequency.The spread of the hardware simulation is 7 times faster than than of the software simulation.

关键词

人工智能视觉芯片深度学习MobileNetZeBu

Keywords

AI vision chipdeep learningMobileNetZeBu

references

KRIZHEVSK Y A, SUTSKEVER I, HINTON G E . ImageNet classification with deep convolutional neural networks[J]. Communication softhe ACM, 2017,60(6): 84-90.

SZEGED Y C, LIU W, JIA Y Q ,et al. Going deeper with convolutions[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2015: 1-9.

DONG X D, XU Y P, XU Z J ,et al. A static hand gesture recognition model based on the improved centroid watershed algorithm and a dual-channel CNN[C]// Proceedings of 2018 24th International Conference on Automation and Computing (ICAC). Piscataway:IEEEPress, 2018: 1-6.

REN S Q, HE K M, GIRSHICK R ,et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,39(6): 1137-1149.

SHIN H C, ROTH H R, GAO M C ,et al. Deep convolutional neural networks for computer-aided detection:CNN architectures,dataset characteristics and transfer learning[J]. IEEE Transactions on Medical Imaging, 2016,35(5): 1285-1298.

LIN T Y, ROYCHOWDHURY A, MAJI S . Bilinear CNN models for fine-grained visual recognition[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015: 1449-1457.

WE IH, ZHU M, WANG B ,et al. Two-level progressive attention convolutional network for fine-grained image recognition[J]. IEEE Access, 2020(8): 104985-104995.

ZHANG Y, YANG S Y, LI H B ,et al. Shadow tracking of moving target based on CNN for video SAR system[C]// Proceedings of IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. Piscataway:IEEE Press, 2018: 4399-4402.

WANG M, ABDELFATTAH S, MOUSTAFA N ,et al. Deep Gaussian mixture-hidden Markov model for classification of EEG signals[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018,2(4): 278-287.

CHEN L, ZHANG H W, XIAO J ,et al. SCA-CNN:spatial and channel-wise attention in convolutional networks for image captioning[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 6298-6306.

ZHANG X Y, ZHOU X Y, LIN M X ,et al. ShuffleNet:an extremely efficient convolutional neural network for mobile devices[C]// Proceedings of 2018 IEEE/CVFConference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 6848-6856.

SUDHA S, JAYANTHI K B, RAJASEKARAN C ,et al. Segmentation of RoI in medical images using CNN-A comparative study[C]// Proceedings of TENCON 2019 - 2019 IEEE Region 10 Conference. Piscataway:IEEE Press, 2019: 767-771.

SHARMA A K, FOROOSH H . Slim-CNN:alight-weight CNN for face attribute prediction[C]// Proceedings of 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition. Piscataway:IEEE Press, 2020: 329-335.

HOWARD A G, ZHU M L, CHEN B ,et al. MobileNets:efficient convolutional neural networks for mobile vision applications[EB]. 2017.

SHI C, YANG J, HAN Y ,et al. A 1000 fps vision chip based on a dynamically reconfigurable hybrid architecture comprising a PE array processor and self-organizing map neural network[J]. IEEE Journal of Solid-State Circuits, 2014,49(9): 2067-2082.

LIHL , ZHANGZ X, YANG J ,et al. A novel vision chip architecture for image recognition based on convolutional neural network[C]// Proceedings of 2015 IEEE 11th International Conference on ASIC. Piscataway:IEEE Press, 2015: 1-4.

VINAY B K, HARIHARM , KILLEDAR A . The FPGA based emulation of complex SoC for ADAS market on ZeBu-Server[C]// Proceedings of 2014 International Conference on Advances in Electronics Computers and Communications. Piscataway:IEEE Press, 2014: 1-4.

ENDC. Synopsys推出业界最快的仿真系统[EB]. 2014.

ENDC. Synopsys Iaunches the industry’s fastest simulation system[EB]. 2014.

WANG S H, JIANG Y Y, HOUXX ,et al. Cerebral micro-bleed detection based on the convolution neural network with rank based average pooling[J]. IEEE Access, 2017(5): 16576-16583.

BASHA S H S, DUBEY S R, PULABAIGARI V ,et al. Impact of fully connected layers on performance of convolutional neural networks for image classification[J]. Neurocomputing, 2020,378: 112-119.

ZEILER M D, FERGUS R . Visualizing and understanding convolutional networks[C]// Computer Vision – ECCV 2014, 2014: 818-833.

SIMONYANK ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[EB]. 2014.

SZEGEDY C, VANHOUCKEV , IOFFE S ,et al. Rethinking the inception architecture for computer vision[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 2818-2826.

HE K M, ZHANG X Y, REN S Q ,et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 770-778.

ZOPH B, VASUDEVAN V, SHLENS J ,et al. Learning transferable architectures for scalable image recognition[C]// Proceedings of 2018 IEEE/CVFConference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 8697-8710.

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