Direct-to-Event Spiking Neural Network Transfer

• URL: http://arxiv.org/abs/2605.07207v1
• Date: Fri, 08 May 2026 03:54:25 GMT
• Title: Direct-to-Event Spiking Neural Network Transfer
• Abstract summary: Spiking Neural Networks (SNNs) have gained increasing attention due to their potential for low-power computation on neuromorphic hardware.<n>A widely adopted training strategy for SNNs is direct coding, which enable backpropagation on neuron implementations.<n>Recent studies have shown that direct-coded SNNs remain substantially less energy-efficient than their event-based counterparts.
• Score: 0.20999222360659606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Neural Networks (SNNs) have gained increasing attention due to their potential for low-power computation on neuromorphic hardware. A widely adopted training strategy for SNNs is direct coding, which enable backpropagation on neuron implementations using continuous-valued surrogate activations. However, recent studies have shown that direct-coded SNNs remain substantially less energy-efficient than their event-based counterparts, limiting their practical deployment in energy sensitive scenarios. Still, to promote the reusability of pretrained SNN database on direct code, this motivates an important yet underexplored question: How can a SNN pretrained with direct code be effectively converted into an event-based representation? In this research, we present the first systematic investigation into this transfer problem, analyze the key challenges that arise when transitioning from direct-coded to event-based computation and propose a set of methods to enable energy-efficient transfer while preserving model performance.

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