用于生物医学边缘推理的可重构混合卷积-全连接神经形态核心

中文摘要 AI 辅助

本研究提出了一种基于FPGA的可编程架构，用于脉冲卷积神经网络（SCNN）推理，将实时缺氧分类作为生物医学边缘应用。该架构在可编程、量化、基于层的神经形态硬件核心上实现了混合脉冲卷积-全连接（CNN-FC）拓扑：早期层采用感受野连接执行脉冲卷积，支持多通道卷积核和步长；深层采用全连接脉冲层进行分类。基于PyTorch的软硬件协同设计流程支持部署经量化且可配置的训练参数。该设计首先在MNIST和Fashion-MNIST数据集上验证，16位精度下硬件准确率分别达98%和86%；随后将其应用于肩部传感器采集的红色及红外光体积描记（PPG）信号的缺氧分类，同时将肤色作为额外输入通道，在16位精度下，五折交叉验证的平均硬件准确率达88.26%，动态功耗为1.455 W，证明了在边缘设备上实现低功耗神经形态生物医学分类的可行性。

英文摘要

This work presents a programmable FPGA-based architecture for spiking convolutional neural network (SCNN) inference, with real-time hypoxia classification serving as a biomedical edge application. The architecture implements a hybrid spiking convolutional-fully connected (CNN-FC) topology on a programmable, quantized, layer-based neuromorphic hardware core. Early layers perform spiking convolution using receptive-field connectivity with support for multi-channel kernels and stride, while deeper layers use fully connected spiking layers for classification. A PyTorch-based hardware-software co-design flow enables deployment of trained parameters with quantization and configurability support. The design is first validated on MNIST and Fashion-MNIST, achieving hardware accuracies of up to 98% and 86%, respectively, at 16-bit precision. It is then applied to hypoxia classification using red and infrared photoplethysmography (PPG) signals acquired from a shoulder-mounted sensor, with skin tone included as an additional input channel. The resulting classifier achieves an average hardware accuracy of 88.26% across five folds at 16-bit precision while consuming 1.455 W of dynamic power, demonstrating the feasibility of low-power neuromorphic biomedical classification at the edge.

发表机构

• Drexel University（德雷塞尔大学）

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