面向心电检测的混合多模卷积神经网络加速器设计

刘冬生; 魏来; 邹雪城; 陆家昊; 成轩; 胡昂; 李德建; 赵旭; 蒋曲明

doi:10.11999/JEIT220534

面向心电检测的混合多模卷积神经网络加速器设计

doi: 10.11999/JEIT220534

1.
华中科技大学光学与电子信息学院武汉 430074
2.
国家电网电力工业芯片设计与分析技术重点实验室，北京智芯微电子科技有限公司北京 100000
3.
楚天龙股份有限公司东莞 523000

基金项目: 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)

详细信息

作者简介:
刘冬生：男，博士，教授，研究方向为能效无线传输技术及芯片设计、后量子密码算法及密码芯片设计

魏来：男，硕士生，研究方向为CNN硬件加速

邹雪城：男，博士，教授，研究方向为超大规模集成电路

陆家昊：男，博士生，研究方向为AI架构与处理器

成轩：男，硕士生，研究方向为AI算法高效硬件实现

胡昂：男，博士，研究方向为射频SoC

李德建：女，硕士，研究方向为载波通信、低功耗设计

赵旭：男，博士，研究方向为载波通信、无线通信

蒋曲明：女，硕士，研究方向为电磁场与微波技术

通讯作者:
魏来　wei1123212130@163.com

中图分类号: TN402
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出版历程
- 收稿日期: 2022-04-28
- 修回日期: 2022-06-18
- 网络出版日期: 2022-06-28
- 刊出日期: 2023-01-17

Design of Hybrid Multimode Convolutional Neural Network Accelerator for Electrocardiogram Detection

1.
School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
2.
State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-chip Microelectronics Technology Co. Ltd., Beijing 100000, China
3.
Chutian Dragon Co. Ltd., Dongguan 523000, China

Funds: The National Natural Science Foundation of China (62134002), The National Key Research and Development Program of China (2021YFA0715502), The Key Research and Development Project of Hubei Province (YFYB2020000413), The Introduced Innovative R&D Team Program of Dongguan (201760712600139)

摘要

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。
- 卷积神经网络 /
- 心电信号分类 /
- 卷积循环展开 /
- 硬件实现
Abstract: 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 one-Dimensional 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. At 200 MHz operating frequency, the overall performance can reach 28.1 GOPS, and the hardware efficiency reaches 12.82 GOPS/kLUT.
- Convolutional Neural Network(CNN) /
- ElectroCardioGram(ECG) signal classification /
- Convolutional loop unrolling /
- Hardware implementation

HTML全文

图 1 各类型ECG信号的波形特征

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图 2 卷积层运算的4级循环

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图 3 全连接层运算的两级循环

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图 4 1D-CNN模型结构图^[13]

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图 5 各层计算对应的运算模式

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图 6 3D-PE阵列结构

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图 7 CNN加速器的总体硬件架构

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图 8 FPGA硬件分类结果的混淆矩阵

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表 1 软硬件平台测试集的性能指标(%)

平台	Acc	Sen	Spec	Ppr
Intel Core i5-10400	99.20	97.97	99.60	98.80
ZYNQ 7000 ZC706	99.19	97.92	99.60	98.80

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表 2 CNN加速器性能比较

文献	FPGA	Bit-width	CNN Model	Clock (MHz)	DSP Utilization	Resource (kLUT)	Throughput (GOPS)	Efficiency (GOPS/kLUT)	DSP Efficiency (GOPS/DSP)
[7]	Arria 10 GX 1150	16 bit Fixed	VGG-16	200	3036	235k	1030.2	4.384	0.339
[17]	Zynq XC7Z045	16 bit Fixed	VGG-16	150	780	183k	137.0	0.749	0.175
[18]	Zynq ZCU 102	16 bit Fixed	VGG-16	200	1352	390k	495.4	1.270	0.366
[19]	Zynq XC7Z045	16 bit Fixed	VGG-16	172	576	59k	316.2	5.358	0.549
本设计	Zynq ZC706	16 bit Fixed	1D-CNN	200	80	2.2k	28.1	12.820	0.360

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