用脉冲神经网络实现高精度低功耗的艾美耳虫检测
Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture
- 先将预训练CNN转为脉冲特征提取器，再用无监督脉冲网络分类
- 98.32%准确率，能耗比传统ANN降低223倍以上
- 适合部署在神经形态硬件上的便携式养殖病害诊断系统
由艾美耳虫引起的球虫病对家禽和兔子产业构成重大威胁，亟需快速精准的诊断工具。尽管深度学习模型精度高，但其高能耗限制了在资源受限环境中的应用。本文提出一种两阶段脉冲神经网络（SNN）架构：首先将预训练卷积神经网络转换为脉冲特征提取器，再与轻量级、基于脉冲时序依赖可塑性（STDP）训练的无监督SNN分类器结合。该模型达到98.32%的艾美耳虫分类准确率，且相比传统人工神经网络（ANN），能耗降低超过223倍。本工作展示了高精度与极致能效的协同潜力，为神经形态硬件上自主低功耗诊断系统的发展铺平道路。
原文摘要 · Abstract (English)
Coccidiosis, a disease caused by the Eimeria parasite, represents a major threat to the poultry and rabbit industries, demanding rapid and accurate diagnostic tools. While deep learning models offer high precision, their significant energy consumption limits their deployment in resource-constrained environments. This paper introduces a novel two-stage Spiking Neural Network (SNN) architecture, where a pre-trained Convolutional Neural Network is first converted into a spiking feature extractor and then coupled with a lightweight, unsupervised SNN classifier trained with Spike-Timing-Dependent Plasticity (STDP). The proposed model sets a new state-of-the-art, achieving 98.32\% accuracy in Eimeria classification. Remarkably, this performance is accomplished with a significant reduction in energy consumption, showing an improvement of more than 223 times compared to its traditional ANN counterpart. This work demonstrates a powerful synergy between high accuracy and extreme energy efficiency, paving the way for autonomous, low-power diagnostic systems on neuromorphic hardware.
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