Verification of an artificial intelligence vision chip design for edge computing based on hardware simulation system

Xuanzhe XU; Ke NING; Xuemin ZHENG; Mingxin ZHAO; Mengmeng XU; Nanjian WU; Liyuan LIU

doi:10.11959/j.issn.2096-3750.2022.00250

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Verification of an artificial intelligence vision chip design for edge computing based on hardware simulation system

Topic: IoT Terminals and Chips | 更新时间：2024-06-05

- Verification of an artificial intelligence vision chip design for edge computing based on hardware simulation system
- Chinese Journal on Internet of Things Vol. 6, Issue 1, Pages: 20-28(2022)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2022.00250
  CLC： TN47
- Published：30 March 2022，
  
  Published Online：2022-03，
- 稿件说明：
移动端阅览
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：

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

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