This paper explores the synergistic potential of neuromorphic and edge computing to create a versatile machine learning (ML) system tailored for processing data captured by dynamic vision sensors. We construct and train hybrid models, blending spiking neural networks (SNNs) and artificial neural networks (ANNs) using PyTorch and Lava frameworks. Our hybrid architecture integrates an SNN for temporal feature extraction and an ANN for classification. We delve into the challenges of deploying such hybrid structures on hardware. Specifically, we deploy individual components on Intel's Neuromorphic Processor Loihi (for SNN) and Jetson Nano (for ANN). We also propose an accumulator circuit to transfer data from the spiking to the non-spiking domain. Furthermore, we conduct comprehensive performance analyses of hybrid SNN-ANN models on a heterogeneous system of neuromorphic and edge AI hardware, evaluating accuracy, latency, power, and energy consumption. Our findings demonstrate that the hybrid spiking networks surpass the baseline ANN model across all metrics and outperform the baseline SNN model in accuracy and latency.

本研究探讨了神经形态计算和边缘计算相结合的潜力，创建了一个适用于处理动态视觉传感器捕获数据的多功能机器学习系统。通过混合使用PyTorch和Lava框架构建和训练了混合模型，融合脉冲神经网络（SNNs）和人工神经网络（ANNs）。该研究还解决了在硬件上部署这种混合结构的挑战，并在Intel的神经形态处理器Loihi和Jetson Nano上部署了相应组件。此外，该研究在神经形态计算和边缘人工智能硬件的异构系统上进行了全面的性能分析，包括准确性、延迟、功耗和能源消耗。研究结果表明，混合脉冲神经网络在所有指标上均优于基准人工神经网络模型，并在准确性和延迟方面优于基准脉冲神经网络模型。

在神经形态学和边缘 AI 硬件上有效部署混合 SNN