摘要
随着医疗资源日益匮乏以及人口老龄化日趋严重,心血管疾病已对人类健康造成了极大的威胁。具有心电(ECG)检测的便携式设备能有效降低心血管疾病对患者的威胁,因此该文设计了一种面向心电检测的混合多模卷积神经网络加速器。该文首先介绍了一种用于心电信号分类的1维卷积神经网络(1D-CNN)模型,随后针对该模型设计了一种高效的卷积神经网络(CNN)加速器,该加速器采用了一种多并行展开策略和多数据流的运算模式完成了卷积循环的加速和优化,能在时间上和空间上高度复用数据,同时提高了硬件资源利用率,从而提升了硬件加速器的硬件效率。最后基于Xilinx ZC706硬件平台完成了原型验证,结果显示,所设计卷积神经网络加速器消耗的资源为2247 LUTs, 80 DSPs。在200 MHz的工作频率下,该设计的整体性能可达到28.1 GOPS,并且硬件效率达到了12.82 GOPS/kLUT。
With the increasing scarcity of medical resources and the aging of the population, cardiovascular disease has posed a great threat to human health. Portable devices with ElectroCardioGram(ECG) detection can effectively reduce the threat of cardiovascular disease to patients. In this paper, a hybrid multi-mode Convolutional Neural Network(CNN) accelerator is designed for monitoring the patient’s ECG. Firstly, a oneDimensional Convolutional Neural Network(1D-CNN) model is introduced for ECG classification, then an efficient accelerator is designed for this model, which adopts a multi-parallel expansion strategy and multi-data stream operation mode to complete the acceleration and optimization of convolution loops. The proposed operation mode can highly reuse data in time and space, and improve the utilization of hardware resources, thereby improving the hardware efficiency of the hardware accelerator. Finally, the prototype verification is completed based on the Xilinx ZC706 hardware platform. The results show 2247 LUTs and 80 DSPs are consumed. At200 MHz operating frequency, the overall performance can reach 28.1 GOPS, and the hardware efficiency reaches 12.82 GOPS/kLUT.
作者
刘冬生
魏来
邹雪城
陆家昊
成轩
胡昂
李德建
赵旭
蒋曲明
LIU Dongsheng;WEI Lai;ZOU Xuecheng;LU Jiahao;CHENG Xuan;HU Ang;LI Dejian;ZHAO Xu;JIANG Quming(School of Optical and Electronic Information,Huazhong University of Science and Technology,Wuhan 430074,China;State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology,Beijing Smart-chip Microelectronics Technology Co.Ltd.,Beijing 100000,China;Chutian Dragon Co.Ltd.,Dongguan 523000,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2023年第1期33-41,共9页
Journal of Electronics & Information Technology
基金
国家自然科学基金(62134002)
国家重点研发计划(2021YFA0715502)
湖北省重点研发项目(YFYB2020000413)
东莞引进创新科研团队计划(201760712600139)。
关键词
卷积神经网络
心电信号分类
卷积循环展开
硬件实现
Convolutional Neural Network(CNN)
ElectroCardioGram(ECG)signal classification
Convolutional loop unrolling
Hardware implementation
