NeuromorphicRT: A Production-Grade Spiking Neural Network Runtime for Energy-Efficient Edge AI

NeuromorphicRT is a production-grade spiking neural network (SNN) runtime for energy-efficient edge AI on resource-constrained hardware. This paper presents the framework architecture, which supports five neuron models (LIF, AdEx, Izhikevich, TTFS, Surrogate Gradient), 11 model architectures spanning vision, audio, and anomaly detection, and a Temporal Event Neural Network (TENN) with learnable temporal kernels. We demonstrate up to 1000x energy reduction compared to conventional DNNs on edge targets including Jetson Orin Nano and Raspberry Pi 5. Training results on CIFAR-10 using surrogate gradient backpropagation through time are reported, achieving 61.76% validation accuracy with a 129K-parameter SpikeNet7 model trained on Jetson AGX Orin. The framework includes DNN-to-SNN conversion, event-driven delta inference, and a selective state-space model (Mamba-style) with spike I/O.