[1]宫大汉,于龙龙,陈辉,等.面向车规级芯片的对象检测模型优化方法[J].智能系统学报,2021,16(5):900-907.[doi:10.11992/tis.202107057]
 GONG Dahan,YU Longlong,CHEN Hui,et al.Object detection model optimization method for car-level chips[J].CAAI Transactions on Intelligent Systems,2021,16(5):900-907.[doi:10.11992/tis.202107057]
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面向车规级芯片的对象检测模型优化方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年5期
页码:
900-907
栏目:
吴文俊人工智能科技进步奖一等奖
出版日期:
2021-09-05

文章信息/Info

Title:
Object detection model optimization method for car-level chips
作者:
宫大汉12 于龙龙3 陈辉24 杨帆12 骆沛5 丁贵广12
1. 清华大学 软件学院, 北京 100084;
2. 清华大学 北京信息科学与技术国家研究中心,北京 100084;
3. 涿溪脑与智能研究所,浙江 杭州 311121;
4. 清华大学 自动化系,北京 100084;
5. 禾多科技(北京)有限公司,北京 100102
Author(s):
GONG Dahan12 YU Longlong3 CHEN Hui24 YANG Fan12 LUO Pei5 DING Guiguang12
1. School of Software, Tsinghua University, Beijing 100084, China;
2. BNRist Tsinghua University, Beijing 100084, China;
3. Zhuoxi Institute of Brain and Intelligence, Hangzhou 311121, China;
4. Department of Automation, Tsinghua University, Beijing 100084, China;
5. HoloMatic Technology (Beijing) Co., Ltd, Beijing 100102, China
关键词:
人工智能计算机视觉对象检测终端设备车规级芯片卷积神经网络模型加速模型量化
Keywords:
artificial intelligencecomputer visionobject detectionterminal equipmentcar-level chipconvolutional neural networkmodel accelerationmodel quantization
分类号:
TP18
DOI:
10.11992/tis.202107057
摘要:
卷积神经网络复杂的网络结构使得模型计算复杂度高,限制了其在自动驾驶等实际终端场景中的应用。针对终端场景下的计算资源受限的问题,本文从轻量化深度模型设计和车规级芯片模型部署验证两方面进行研究。针对深度模型计算效率和检测精度的矛盾,本文设计了基于中心卷积的轻量化对象检测模型,实现功耗低且精度高的模型性能。进一步,本文基于量化感知训练的模型加速部署方法在车规级芯片上开展了系统级部署验证,在车规级芯片tda4上成功实现了高效的对象检测模型,在自动驾驶场景中取得了良好的性能。
Abstract:
Convolutional neural networks have achieved great success in visual perception tasks. Its complex network structure makes the model computationally complex, which limits its application in actual terminal scenarios such as autonomous driving. Aiming at the problem of limited computing resources in terminal scenarios, in this paper, we conduct research from two aspects: lightweight deep model design and the model deployment and verification on car-level chips. As for the contradiction between the calculation efficiency of deep models and the detection accuracy, we design a lightweight object detection model based on the center-convolution, enjoying low power consumption and high accuracy model performance. Furthermore, based on the method of quantization aware training, we carried out system-level deployment and verification on car-level chips. We successfully implemented a high-efficiency object detection model on the car-level chips, i.e. tda4, and achieved good performance in autonomous driving scenarios.

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备注/Memo

备注/Memo:
收稿日期:2021-07-27。
基金项目:国家自然科学基金项目(U1936202,61925107);中国博士后科学基金创新人才计划项目(BX2021161)
作者简介:宫大汉,博士研究生,主要研究方向为轻量化深度模型结构设计和边缘设备智能推理引擎构建;于龙龙,算法工程师,主要方向为嵌入式智能设备开发和模型部署;丁贵广,副教授,博士,主要研究方向为多媒体信息处理、计算机视觉感知。获国家科技进步二等奖1项、人工智能学会科技进步奖一等奖1项、中国电子学会技术发明一等奖1项。主持和参与基金委杰出青年科学基金项目、基金委重点项目、重点研发项目等国家级项目数十项。发表学术论文近百篇。
通讯作者:丁贵广.E-mail:dinggg@tsinghua.edu.cn
更新日期/Last Update: 1900-01-01