Design of FPGA Based Convolutional Neural Network Co-Processor

doi:10.7652/xjtuxb201807022

您当前的位置：

首页 >

文章列表页 >

Design of FPGA Based Convolutional Neural Network Co-Processor

更新时间：2025-07-09

- Design of FPGA Based Convolutional Neural Network Co-Processor
- Vol. 52, Issue 7, Pages: 153-159(2018)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201807022
  CLC： TP391
- Online First：10 July 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
Design of FPGA Based Convolutional Neural Network Co-Processor[J]. 2018, 52(7): 153-159.
DOI：

Design of FPGA Based Convolutional Neural Network Co-Processor[J]. 2018, 52(7): 153-159. DOI： 10.7652/xjtuxb201807022.

摘要

针对大数据时代下深层次大规模深度学习网络模型在预测中对运算资源和访存带宽需求指数的增长

以及业界传统CPU+GPU解决方案难以应用于日益普遍的移动嵌入式应用场景等问题

提出了一个基于可编程逻辑器件(FPGA)的卷积神经网络协处理器异构加速设计方案。该方案采用通用模型设计思想

具有可编程性

并且能够兼容多种网路模型从而实现硬件加速; 方案具有可扩展性

可在硬件资源允许的范围内进行多核扩展以获得性能翻倍提升。利用硬件的并行性

数据的复用性设计的卷积运算模块提高了硬件资源利用率及运算效率; 合理配置的多级缓存结构降低了协处理器对外部存储器读写频率和带宽的占用率

提升了模块内部的通信效能。在XILINX VC707评估板的上板进行实验

结果表明

MNIST-LeNet测试集的准确率高达99%

CIFAR-10可实现80%

浮点运算速度为5.511×10

-1

综合性能约两倍于Intel Xeno E5-2640 V4服务器通用处理器

达到同期FPGA解决方案的主流水平。

Abstract

In the era of big data

the demand for computing resources and memory bandwidth in deep-level and large-scale deep learning network models is increasing exponentially. Traditional industry solution CPU+GPU is not suitable to the prevalent scenarios of mobile embedded applications. To deal with this problem

we proposed a design of convolutional neural network co-processor based on FPGA programmable logic device. This solution focuses on high compatibility. It has programmability and is compatible with a variety of network models to achieve hardware acceleration. It also has scalability to allow multi-core expansion within the range of hardware resources to achieve double

performance. The design of convolutional operation module focuses on hardware parallelism and data reusability

which improves the utilization of hardware resources and computing efficiency. Rationally configured multi-level buffer structure reduces the co-processor's occupancy rate of external memory's read/write frequency and bandwidth

improves the internal communication efficiency of the module. The experimental results on the XILINX VC707 evaluation board show that the accuracy of the test set is 99%

the CIFAR-10 can achieve 80%

and the peak computing capability is 5.511×10

-1

the overall performance is approximately twice that of the general-purpose processor of Intel Xeno E5-2640 V4 server. Moreover

the processing performance of our design reaches the current mainstream level of FPGA solutions.

关键词

Keywords

references

RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors [J]. Nature, 1986, 323(6088): 533-536.

LIANG P, VERHELST M. Session 14 overview: next-generation processing [C]∥Solid-State Circuits Conference. Piscataway, NJ, USA: IEEE, 2016: 252-253.

DALY D, FUJINO L. ISSCC 2017: intelligent chips for a smart world [J]. IEEE Solid-State Circuits Magazine, 2016, 8(4): 92-93.

FRIEDMAN D. Hardware approaches to machine learning and inference [C]∥2018 IEEE International Solid-State Circuits Conference. Piscataway, NJ, USA: IEEE, 2018: 2376-8606.

CHEN T, DU Z, SUN N, et al. DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning [J]. ACM SIGPLAN Notices, 2014, 49(4): 269-284.

陆奇. 百度大脑是百度AI平台核心智能云有机会颠覆云市场 [J]. 信息与电脑(理论版), 2017(14): 1-2.

LU Qi. Baidu's brain is the core of Baidu's AI platform Smart Cloud has the opportunity to subvert the cloud market [J]. China Computer Communication, 2017(14): 1-2.

佚名. 阿里巴巴携手英特尔开发一款基于FPGA的解决方案 [J]. 中国电子商情: 基础电子, 2017(3): 30.

Anonymous. Alibaba teamed up with Intel to develop an FPGA-based solution [J]. China Electronics Market: Basic Electronics, 2017(3): 30.

卢宏涛, 张秦川. 深度卷积神经网络在计算机视觉中的应用研究综述 [J]. 数据采集与处理, 2016, 31(1): 1-17.

LU Hongtao, ZHANG Qinchuan. Applications of deep convolutional neural network in computer vision [J]. Journal of Data Acquisition and Processing, 2016, 31(1): 1-17.

ANTHIMOPOULOS M, CHRISTODOULIDIS S, EBNER L, et al. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network [J]. IEEE Transactions on Medical Imaging, 2016, 35(5): 1207.

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [C]∥Advances in Neural Information Processing Systems. Piscataway, NJ, USA: IEEE, 2012: 1097-1105.

LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2015: 3431-3440.

余奇. 基于FPGA的深度学习加速器设计与实现 [D]. 合肥: 中国科学技术大学, 2016: 5-20.

陈建英, 杨宪泽, 张楠. 面向大规模分布式系统的多级缓存信息结构研究 [J]. 西南民族大学学报(自然科学版), 2012, 38(3): 457-460.

CHEN Jianying, YANG Xianze, ZHANG Nan. Research on multi-level structure of cache information in large-scale distributed system [J]. Journal of Southwest University for Nationalities, 2012, 38(3): 457-460.

ZHANG C, LI P, SUN G, et al. Optimizing FPGA-based accelerator design for deep convolutional neural networks [C]∥Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. New York, USA: ACM, 2015: 161-170.

WANG D, AN J, XU K. PipeCNN: an OpenCL-based FPGA accelerator for large-scale convolution neuron networks [EB/OL]. [2018-03-16]. http:∥pdfs. semanticscholar.org/8d6d/df21989e9b5bd15e4bf f972e2370d5dc47d2.pdf.

QIU J, WANG J, YAO S, et al. Going deeper with embedded FPGA platform for convolutional neural network [C]∥Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. New York, USA: ACM, 2016: 26-35.

王亚昆,吴爱国,董娜.吸收式制冷机组逆神经网络设定优化.2018,52(1):123-128.[doi:10.7652/xjtuxb201801018]

曹玉良,明廷锋,贺国,等.基于深度学习的离心泵空化状态识别.2017,51(11):165-172.[doi:10.7652/xjtuxb201711 023]

王丽华,谢阳阳,张永宏,等.采用深度学习的异步电机故障诊断方法.2017,51(10):128-134.[doi:10.7652/xjtuxb2017 10021]

章军辉,李庆,陈大鹏.基于BP神经网络的纵向避撞安全辅助算法.2017,51(7):140-147.[doi:10.7652/xjtuxb201707 020]

李晓娟,孙文磊,袁逸萍,等.扰动环境下作业车间网络多瓶颈识别方法研究.2016,50(12):64-72.[doi:10.7652/xjtuxb 201612011]

孙泽宇,伍卫国,曹仰杰,等.无线传感器网络中能量均衡参数可控覆盖算法.2016,50(8):77-83.[doi:10.7652/xjtuxb 201608013]

张小栋,郭晋,李睿,等.表情驱动下脑电信号的建模仿真及分类识别.2016,50(6):1-8.[doi:10.7652/xjtuxb201606001]

姜涛,黄伟,王安麟.多路阀阀芯节流槽拓扑结构组合的神经网络模型.2016,50(6):36-41.[doi:10.7652/xjtuxb201606 006]

张鹏,陈湘军,阮雅端,等.采用稀疏SIFT特征的车型识别方法.2015,49(12):137-143.[doi:10.7652/xjtuxb201512 022]

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Bearing Fault Diagnosis Method of Deep Convolutional Neural Network Based on Multiwavelet Decomposition

Intelligent Quantitative Non-Destructive Evaluation Method for Delamination of Carbon Fiber Composite Tube with Mirror-Reflection Infrared Thermography

Model Predictive Control-Oriented Deep Koopman Global Linear Modeling Method for Wind Turbines

Dynamic Liquid Level Prediction of Steam Generator under Main Steam Pipe Rupture Accidents

A Pure Convolutional Model Fused with U-net Network for Video Prediction

Related Author

TAO Tangfei

ZHOU Wenjie

KUANG Jiachen

XU Guanghua

YANG Yang

CHEN Hongen

LIU Bofan

TONG Zongfei

Related Institution

西安交通大学机械工程学院

西安交通大学现代设计及转子轴承系统教育部重点实验室

School of Aerospace Engineering，Xi’an Jiaotong University

State Key Laboratory for Strength and Vibration of Mechanical Structures，Xi’an Jiaotong University

Shaanxi Engineering Research Center of Nondestructive Testing and Structural Integrity Evaluation，Xi’an Jiaotong University

⁰