# Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware

arXiv: https://arxiv.org/abs/2006.06777 · HTML: https://mlsanity.com/paper/2006.06777/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

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

- [Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants](https://mlsanity.com/paper/2202.10570/) (2022)

## Abstract (arXiv)

In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at run-time. Our design methodology operates in two steps -- step 1 is a layer-wise greedy approach to partition SNNs into clusters of neurons and synapses incorporating the constraints of the neuromorphic architecture, and step 2 is a hill-climbing optimization algorithm that minimizes the total spikes communicated between clusters, improving energy consumption on the shared interconnect of the architecture. We conduct experiments to evaluate the feasibility of our algorithm using synthetic and realistic SNN-based applications. We demonstrate that our algorithm reduces SNN mapping time by an average 780x compared to a state-of-the-art design-time based SNN partitioning approach with only 6.25% lower solution quality.