Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915355056668672 |
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| author | Derzhavin, Andrey Larionov, Denis |
| author_facet | Derzhavin, Andrey Larionov, Denis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi Derzhavin, Andrey Larionov, Denis Neural and Evolutionary Computing Artificial Intelligence 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. |
| title | Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2506.18306 |