Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi

June 23, 2025 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors Andrey Derzhavin, Denis Larionov arXiv ID 2506.18306 Category cs.NE: Neural & Evolutionary Cross-listed cs.AI Citations 0 Venue arXiv.org Last Checked 4 months ago
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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