A Reconfigurable Hybrid Convolutional-Fully Connected Neuromorphic Core for Biomedical Edge Inference

Abstract

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.