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arXiv · 2503.04341

Eventprop training for efficient neuromorphic applications

Thomas Shoesmith·James C. Knight·Balázs Mészáros·Jonathan Timcheck·Thomas Nowotny
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Abstract

Neuromorphic computing can reduce the energy requirements of neural networks and holds the promise to `repatriate' AI workloads back from the cloud to the edge. However, training neural networks on neuromorphic hardware has remained elusive. Here, we instead present a pipeline for training spiking neural networks on GPUs, using the efficient event-driven Eventprop algorithm implemented in mlGeNN, and deploying them on Intel's Loihi 2 neuromorphic chip. Our benchmarking on keyword spotting tasks indicates that there is almost no loss in accuracy between GPU and Loihi 2 implementations and that classifying a sample on Loihi 2 is up to 10X faster and uses 200X less energy than on an NVIDIA Jetson Orin Nano.

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Thomas Shoesmith, James C. Knight, Balázs Mészáros, Jonathan Timcheck, Thomas Nowotny. 2025-03-06. Eventprop training for efficient neuromorphic applications. https://arxiv.org/abs/2503.04341

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