# SpiNNTools: The Execution Engine for the SpiNNaker Platform

arXiv: https://arxiv.org/abs/1810.06835 · HTML: https://mlsanity.com/paper/1810.06835/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning](https://mlsanity.com/paper/2401.04491/) (2024)
- [Demonstrating Analog Inference on the BrainScaleS-2 Mobile System](https://mlsanity.com/paper/2103.15960/) (2021)
- [Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker](https://mlsanity.com/paper/2312.02659/) (2023)
- [A Segmentation Method for fluorescence images without a machine learning approach](https://mlsanity.com/paper/2212.13945/) (2022)

## Abstract (arXiv)

Distributed systems are becoming more common place, as computers typically contain multiple computation processors. The SpiNNaker architecture is such a distributed architecture, containing millions of cores connected with a unique communication network, making it one of the largest neuromorphic computing platforms in the world. Utilising these processors efficiently usually requires expert knowledge of the architecture to generate executable code. This work introduces a set of tools (SpiNNTools) that can map computational work described as a graph in to executable code that runs on this novel machine. The SpiNNaker architecture is highly scalable which in turn produces unique challenges in loading data, executing the mapped problem and the retrieval of data. In this paper we describe these challenges in detail and the solutions implemented.