强激光与粒子束2025，Vol.37Issue(5)：139-149,11.DOI:10.11884/HPLPB202537.240398
核脉冲峰值序列轻量化神经网络核素识别模型及其FPGA加速方法
Lightweight neural network model for nuclide recognition based on nuclear pulse peak sequence and its FPGA acceleration method
摘要
Abstract
Radionuclides have been widely used in the fields of nuclear medicine,nuclear security and non-destructive testing,and their accurate identification is the basis of qualitative detection of radionuclides.In the portable nuclide recognition instrument,the traditional energy spectrum analysis method has the shortcomings of high delay and low recognition rate.This paper proposes a lightweight neural network model for nuclide recognition based on kernel pulse peak sequence and its FPGA hardware acceleration method.A lightweight and efficient neural network model is constructed by introducing depth-separable convolution and reciprocal residual modules,and using global average pooling to replace the traditional fully connected layer.For the network training data set,NaI(Tl)detector model was constructed through Monte Carlo toolkit Geant4 to obtain the analog energy spectrum,and then a simulator generated nuclear pulse signal sequences according to the energy spectrum,and 16 kinds of nuclear pulse signal data were constructed.Finally,the trained model is deployed to PYNQ-Z2 heterogeneous chip through optimization methods such as quantization,fusion and parallel computing to achieve acceleration.Experimental results show that the recognition accuracy of the proposed model can reach 98.3%,which is 13.2%higher than that of the traditional convolutional neural network model,and the number of parameters is only 2 128.After FPGA optimization and acceleration,the single recognition time is 0.273 ms,and the power consumption is 1.94 W.关键词
核素识别/核信号/神经网络/FPGA/硬件加速Key words
nuclide identification/nuclear signal/neural network/FPGA/hardware acceleration分类
能源科技引用本文复制引用
李超,石睿,曾树鑫,徐鑫华,魏雨鸿,庹先国..核脉冲峰值序列轻量化神经网络核素识别模型及其FPGA加速方法[J].强激光与粒子束,2025,37(5):139-149,11.基金项目
国家自然科学基金项目(42074218,42374227) （42074218,42374227）
四川省高等教育人才培养质量和教学改革项目(JG2024-0907) （JG2024-0907）
四川轻化工大学研究生创新基金项目(Y2024251) （Y2024251）
