[1]林广栋,黄光红,陆俊峰.一款人工智能芯片上 FCOS 模型的应用研究[J].计算机技术与发展,2023,33(05):9-15.[doi:10. 3969 / j. issn. 1673-629X. 2023. 05. 002]
　LIN Guang-dong,HUANG Guang-hong,LU Jun-feng.Application of FCOS Model on an AI Chip[J].,2023,33(05):9-15.[doi:10. 3969 / j. issn. 1673-629X. 2023. 05. 002]
点击复制

一款人工智能芯片上 FCOS 模型的应用研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年05期
页码:
9-15
栏目:
嵌入式计算
出版日期:
2023-05-10

文章信息/Info

Title:
Application of FCOS Model on an AI Chip
文章编号:
1673-629X(2023)05-0009-07
作者:
林广栋; 黄光红; 陆俊峰
中国电子科技集团公司第三十八研究所,安徽 合肥 230094
Author(s):
LIN Guang-dong; HUANG Guang-hong; LU Jun-feng
The 38th Institute of China Electronics Technology Group Corporation,Hefei 230094,China)
关键词:
人工智能芯片; 深度学习; 目标检测; DDR; 边缘计算
Keywords:
AI chip; deep learning; object detection; DDR; edge computing
分类号:
TP32
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 05. 002
摘要:
人工智能芯片是专门用于高效执行人工智能计算任务的芯片。 中国电子科技集团公司第三十八所研制了一款针对边缘侧深度学习模型推理计算的人工智能芯片,主要面向雷达图像目标识别、色选机图像智能处理等应用。 该芯片是一个异构的 SOC 芯片,由中央处理核心、神经网络加速核通过片上总线互联形成,峰值算力达到 16TOPS( INT8) 。 FCOS 模型是一个先进的单阶段无锚框目标检测深度学习模型,该模型首次提出的核心原理已经被一些新的目标检测网络模型采用。 该文研究 FCOS 深度学习模型在该人工智能芯片上的部署,并研究片上存储器大小、DDR 带宽、DDR 配置、算力、数据类型等因素对 FCOS 深度学习模型部署的性能和检测效果的影响。 可以为深度学习模型部署技术研究人员、人工智能芯片设计人员提供参考。
Abstract:
AI chip is a kind of chip that can perform artificial intelligence related computing efficiently. The 38th institute of CETCdeveloped an AI chip targeted at deep learning inference tasks at edge devices,and its main application fields includes object detection inradar images,intelligent image processing in color separator devices, etc. The chip is a heterogeneous SOC chip that is composed ofcentral processing unit and neural network accelerator, which are connected together by on - chip bus. Its peak performance achieves16TOPS( INT8) . FCOS is an up - to - date single stage and anchor free object detection deep learning model, the mechanisms firstlyproposed by the model have been utilized in some newer object detection models. The application of FCOS on the chip is studied,and theinfluence of on - chip memory size, DDR bandwidth, DDR configuration, computing power, data type and other factors is thoroughlystudied. This work will provide insights to researchers on deployment of deep learning models and also to designers of AI chips.

相似文献/References:

[1]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(05):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[2]施泽浩,赵启军.基于全卷积网络的目标检测算法[J].计算机技术与发展,2018,28(05):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
　SHI Ze-hao,ZHAO Qi-jun.Object Detection Algorithm Based on Fully Convolutional Neural Network[J].,2018,28(05):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
[3]黄法秀,张世杰,吴志红,等.数据增广下的人脸识别研究[J].计算机技术与发展,2020,30(03):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
　HUANG Fa-xiu,ZHANG Shi-jie,WU Zhi-hong,et al.Research on Face Recognition Based on Data Augmentation[J].,2020,30(05):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
[4]陈浩翔,蔡建明,刘铿然,等. 手写数字深度特征学习与识别[J].计算机技术与发展,2016,26(07):19.
　CHEN Hao-xiang,CAI Jian-ming,LIU Keng-ran,et al. Deep Learning and Recognition of Handwritten Numeral Features[J].,2016,26(05):19.
[5]高翔,陈志,岳文静,等.基于视频场景深度学习的人物语义识别模型[J].计算机技术与发展,2018,28(06):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
　GAO Xiang,CHEN Zhi,YUE Wen-jing,et al.Human Semantic Recognition Model Based on Video Scene Deep Learning[J].,2018,28(05):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
[6]贺飞翔,赵启军. 基于深度学习的头部姿态估计[J].计算机技术与发展,2016,26(11):1.
　HE Fei-xiang,ZHAO Qi-jun. Head Pose Estimation Based on Deep Learning[J].,2016,26(05):1.
[7]徐 融,邱晓晖.一种改进的 YOLO V3 目标检测方法[J].计算机技术与发展,2020,30(07):30.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 007]
　XU Rong,QIU Xiao-hui.An Improved YOLO V3 Object Detection[J].,2020,30(05):30.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 007]
[8]曾志平[] [],萧海东[],张新鹏[]. 基于DBN的金融时序数据建模与决策[J].计算机技术与发展,2017,27(04):1.
　ZENG Zhi-ping[] [],XIAO Hai-dong[],ZHANG Xin-peng[]. Modeling and Decision-making of Financial Time Series Data with DBN[J].,2017,27(05):1.
[9]李全兵,文 钊*,田艳梅*,等.基于 WGAN 的音频关键词识别研究[J].计算机技术与发展,2021,31(08):26.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 005]
　LI Quan-bing,WEN Zhao *,TIAN Yan-mei *,et al.Research on Audio Keywords Recognition Based on WassersteinGenerative Adversarial Network[J].,2021,31(05):26.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 005]
[10]李宏林. 分析式纹理合成技术及其在深度学习的应用[J].计算机技术与发展,2017,27(11):7.
　LI Hong-lin. Analyzed Texture-synthesis Techniques and Their Applications in Deep Learning[J].,2017,27(05):7.

更新日期/Last Update: 2023-05-10