mlsanityData to 14 Oct 2026

Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi

arXiv 2506.18306 ↗First posted 23 Jun 2025cs.NE cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 23 Jun 2025.

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References · 6

  1. 2020The Hardware Lotterycited by 160
  2. 2021SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All Networkscited by 30
  3. 2022Neuromorphic Artificial Intelligence Systemscited by 12
  4. 2024CoLaNET -- A Spiking Neural Network with Columnar Layered Architecture for Classificationcited by 3
  5. 2024Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Examplecited by 3
  6. 2025A Digital Machine Learning Algorithm Simulating Spiking Neural Network CoLaNETcited by 3

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