Year: 2025
Type: conference-paper
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 10× faster and uses 200× less energy than on an NVIDIA Jetson Orin Nano.
Institutions: University of Sussex, Intel (United States)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4412128503