# A Multi-Threading Kernel for Enabling Neuromorphic Edge Applications

arXiv: https://arxiv.org/abs/2510.17745 · HTML: https://mlsanity.com/paper/2510.17745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing](https://mlsanity.com/paper/2103.08392/) (2021)
- [Dynamic Power Management for Neuromorphic Many-Core Systems](https://mlsanity.com/paper/1903.08941/) (2019)

## Built on by (most cited first, 1 of 1)

- [Full Feature Spiking Neural Network Simulation on Micro-Controllers for Neuromorphic Applications at the Edge](https://mlsanity.com/paper/2604.16474/) (2026)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) have sparse, event driven processing that can leverage neuromorphic applications. In this work, we introduce a multi-threading kernel that enables neuromorphic applications running at the edge, meaning they process sensory input directly and without any up-link to or dependency on a cloud service. The kernel shows speed-up gains over single thread processing by a factor of four on moderately sized SNNs and 1.7X on a Synfire network. Furthermore, it load-balances all cores available on multi-core processors, such as ARM, which run today's mobile devices and is up to 70% more energy efficient compared to statical core assignment. The present work can enable the development of edge applications that have low Size, Weight, and Power (SWaP), and can prototype the integration of neuromorphic chips.