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arXiv · 2506.18306

Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi

Andrey Derzhavin·Denis Larionov
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Abstract

This paper presents a lightweight software-based approach for running spiking neural networks (SNNs) without relying on specialized neuromorphic hardware or frameworks. Instead, we implement a specific SNN architecture (CoLaNET) in Rust and optimize it for common computing platforms. As a case study, we demonstrate our implementation, called Spiffy, on a Raspberry Pi using the MNIST dataset. Spiffy achieves 92% accuracy with low latency - just 0.9 ms per training step and 0.45 ms per inference step. The code is open-source.

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Andrey Derzhavin, Denis Larionov. 2025-06-23. Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi. https://arxiv.org/abs/2506.18306

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