Combining Convolution and Delay Learning in Recurrent Spiking Neural Networks

2026-04-17

AI Summary

This work addresses the challenge of deploying conventional recurrent spiking neural networks (SNNs) on resource-constrained edge devices, where large parameter counts and slow inference hinder the balance between efficiency and accuracy. To overcome this limitation, the authors propose a novel convolutional recurrent SNN architecture that, for the first time, integrates convolutional recurrent connections with a learnable axonal delay mechanism, accompanied by a tailored training methodology. Evaluated on audio classification tasks, the proposed model achieves a dramatic reduction in model size—cutting recurrent parameters by approximately 99%—while delivering a 52× speedup in inference latency, all without compromising classification accuracy. This advancement establishes a new paradigm for efficient edge intelligence with spiking neural networks.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Spiking neural networks (SNNs) are rapidly gaining momentum as an alternative to conventional artificial neural networks in resource constrained edge systems. In this work, we continue a recent research line on recurrent SNNs where axonal delays are learned at runtime along with the other network parameters. The first proposed approach, dubbed DelRec, demonstrated the benefit of recurrent delay learning in SNNs. Here, we extend it by advocating the use of convolutional recurrent connections in conjunction with the DelRec delay learning mechanism. According to our tests on an audio classification task, this leads to a streamlined architecture with smaller memory footprint (around 99% savings in terms of number of recurrent parameters) and a much faster (52x) inference time, while retaining DelRec's accuracy. Our code is available at: https://github.com/luciozebendo/delrec_snn/tree/conv_delays

Problem

• Spiking Neural Networks
• Recurrent Connections
• Memory Footprint
• Inference Time
• Edge Systems

Innovation

• convolutional recurrent SNNs
• learnable axonal delays
• edge AI
• parameter efficiency
• spiking neural networks