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

李超; 石睿; 曾树鑫; 徐鑫华; 魏雨鸿; 庹先国

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

doi: 10.11884/HPLPB202537.240398

李超^1,,
石睿^{1, 2, ,},
曾树鑫²,
徐鑫华²,
魏雨鸿²,
庹先国¹

1.
四川轻化工大学物理与电子工程学院，四川宜宾 644000
2.
四川轻化工大学计算机科学与工程学院，四川宜宾 644000

基金项目: 国家自然科学基金项目(42074218,42374227); 四川省高等教育人才培养质量和教学改革项目(JG2024-0907); 四川轻化工大学研究生创新基金项目(Y2024251)

详细信息

作者简介:
李　超，1214395338@qq.com

通讯作者:
石　睿，shirui@suse.edu.cn。

中图分类号: TL81
计量
- 文章访问数: 103
- HTML全文浏览量: 41
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2024-11-17
- 修回日期: 2025-02-24
- 录用日期: 2025-02-24
- 网络出版日期: 2025-03-13
- 刊出日期: 2025-03-31

Lightweight neural network model for nuclide recognition based on nuclear pulse peak sequence and its FPGA acceleration method

Li Chao^1
,,
Shi Rui^{1, 2
, ,},
Zeng Shuxin²,
Xu Xinhua²,
Wei Yuhong²,
Tuo Xianguo¹

1.
College of Physics and Electronic Engineering, Sichuan University of Science and Engineering , Yibin 644000, China
2.
School of Computer Science and Engineering, Sichuan University of Science and Engineering , Yibin 644000, China

摘要

摘要: 放射性核素已在核医疗、核安保及无损检测等领域中广泛应用，而对其准确识别是放射性核素定性检测的基础。在便携式核素识别仪中，基于传统能谱分析方法存在延迟高、识别率低等不足。提出一种基于核脉冲峰值序列的核素识别轻量化神经网络模型及其FPGA硬件加速方法，通过引入深度可分离卷积和倒残差模块，并使用全局平均池化替代传统全连接层，构建了一种轻量化、高效的神经网络模型。针对网络训练数据集，通过蒙特卡罗工具包Geant4构建NaI(Tl)探测器模型，获取模拟能谱，再由核脉冲信号模拟仿真器根据能谱产生核脉冲信号序列，构建了16种核脉冲信号数据。最后，将训练好的模型通过量化、融合与并行计算等优化方法部署到PYNQ-Z2异构芯片，实现加速。实验结果表明，模型识别精度可达98.3%，相较传统卷积神经网络模型提高了13.2%，参数量仅为2 128。FPGA优化加速后单次识别耗时0.273 ms，功耗为1.94 W。
- 核素识别 /
- 核信号 /
- 神经网络 /
- FPGA /
- 硬件加速
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.
- nuclide identification /
- nuclear signal /
- neural network /
- FPGA /
- hardware acceleration

HTML全文

图 1 探测器建模与模拟能谱图

Figure 1. Detector modeling and simulation energy spectrum

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图 2 DT5800D模拟信号流程

Figure 2. DT5800D analog signal flow

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图 3 卷积对比与残差模块对比

Figure 3. Convolution contrast and residual module contrast

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图 4 神经网络结构图

Figure 4. Diagram of neural network structure

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图 5 模型训练过程

Figure 5. Model training process

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图 6 测试集混淆矩阵

Figure 6. Test set confusion matrix

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图 7 流水线示意图

Figure 7. Pipeline diagram

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图 8 分区类型

Figure 8. Partition type

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图 9 HLS部分优化伪代码

Figure 9. HLS partially optimized pseudocode

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图 10 核素识别系统框图

Figure 10. Block diagram of nuclide recognition system

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表 1 模型对比

Table 1. Model comparison

model	recognition accuracy/%	model parameter	model size/kbit
my model	98.3	2128	25.9
LSTM	85.3	27536	111
GhostNet	96.9	1710096	6711
MobileNet-V1	94.1	2396688	9430
ResNet-18	93.8	3856912	15151
VGGNet-16	93.0	34341200	134156

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表 2 量化资源消耗与时延

Table 2. Quantifying resource consumption and delay

resource	32 bit float (utilization rate)	16 bit fixed-point number (utilization rate)
BRAM_18K	240(85%)	79(28%)
DSP	162(73%)	114(51%)
FF	46 657(43%)	12 968(12%)
LUT	51 846(97%)	21 483(40%)
latency(cycles)	38 101	34 792

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表 3 融合前后资源消耗与时延

Table 3. Resource consumption and latency before and after fusion

resource	before fusion(utilization rate)	after fusion(utilization rate)
BRAM_18K	252(90%)	240(85%)
DSP	169(76%)	162(73%)
FF	51006(47%)	46657(43%)
LUT	54753(102%)	51846(97%)
latency(cycles)	47013	38101

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表 4 部分优化前后资源消耗与时延

Table 4. Resource consumption and delay before and after partial optimization

resource	before optimization (utilization rate)	after optimization (utilization rate)
BRAM_18K	0(0%)	0(0%)
DSP	4(1%)	32(14%)
FF	328(～0%)	1 808(1%)
LUT	427(～0%)	802(1%)
latency(cycles)	3 208	811

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表 5 优化前后资源消耗与时延

Table 5. Resource consumption and delay before and after optimization

resource	16 bit fixed-point number (utilization rate)	after-optimization (utilization rate)	VIVADO (utilization rate)
BRAM_18K	79(28%)	109(38%)	95(68%)
DSP	114(51%)	136(61%)	154(70%)
FF	12968(12%)	21099(19%)	15372(14%)
LUT	21483(40%)	39204(73%)	12452(23%)
latency(cycles)	34792	27334	\

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