Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi

2025-06-23

AI Summary

To address the challenges of deploying spiking neural networks (SNNs) efficiently on resource-constrained general-purpose edge devices (e.g., Raspberry Pi), this paper proposes CoLaNET—a lightweight, Rust-based SNN runtime framework integrated with the Spiffy software optimization suite. For the first time, it enables high-accuracy, low-latency SNN execution on commodity CPUs without requiring specialized neuromorphic hardware or frameworks, significantly improving computational efficiency and deployment flexibility. On MNIST, CoLaNET achieves 92% classification accuracy; training and inference per timestep take only 0.9 ms and 0.45 ms, respectively—meeting real-time edge AI requirements. Key contributions include: (1) an efficient, CPU-optimized software implementation paradigm for SNNs; (2) a lightweight architecture co-designed for accuracy and latency; and (3) a fully open-source, reproducible SNN deployment solution for edge platforms.

Technology Category

Cognitive Modeling & Cognitive Systems: Neural Spike CodingMachine Learning: Learning on the Edge & Model CompressionComputer Vision: Learning & Optimization for CV

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsResponsible Web: Human-perceived consequences of algorithmic deployment on the webGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Implement SNNs without neuromorphic hardware
• Optimize CoLaNET for common computing platforms
• Achieve efficient SNN performance on Raspberry Pi

Innovation

• Lightweight software-based SNN implementation
• Rust-optimized CoLaNET for common platforms
• Efficient Raspberry Pi deployment with MNIST