# Mapping Spiking Neural Networks to Neuromorphic Hardware

arXiv: https://arxiv.org/abs/1909.01843 · HTML: https://mlsanity.com/paper/1909.01843/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [Neuromorphic Optical Flow and Real-time Implementation with Event Cameras](https://mlsanity.com/paper/2304.07139/) (2023)
- [Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors](https://mlsanity.com/paper/2303.12167/) (2023)
- [Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning](https://mlsanity.com/paper/2412.09379/) (2024)
- [Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants](https://mlsanity.com/paper/2202.10570/) (2022)
- [Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training](https://mlsanity.com/paper/2205.13585/) (2022)
- [Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence](https://mlsanity.com/paper/2506.01869/) (2025)
- [Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization](https://mlsanity.com/paper/2602.02439/) (2026)
- [Linear Constraints Learning for Spiking Neurons](https://mlsanity.com/paper/2103.12564/) (2021)
- [Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms](https://mlsanity.com/paper/2505.16362/) (2025)
- [Real-Time Neuromorphic Spectrum Intelligence Simulator](https://mlsanity.com/paper/2609.00585/) (2026)

## Abstract (arXiv)

Neuromorphic hardware platforms implement biological neurons and synapses to execute spiking neural networks (SNNs) in an energy-efficient manner. We present SpiNeMap, a design methodology to map SNNs to crossbar-based neuromorphic hardware, minimizing spike latency and energy consumption. SpiNeMap operates in two steps: SpiNeCluster and SpiNePlacer. SpiNeCluster is a heuristic-based clustering technique to partition SNNs into clusters of synapses, where intracluster local synapses are mapped within crossbars of the hardware and inter-cluster global synapses are mapped to the shared interconnect. SpiNeCluster minimizes the number of spikes on global synapses, which reduces spike congestion on the shared interconnect, improving application performance. SpiNePlacer then finds the best placement of local and global synapses on the hardware using a meta-heuristic-based approach to minimize energy consumption and spike latency. We evaluate SpiNeMap using synthetic and realistic SNNs on the DynapSE neuromorphic hardware. We show that SpiNeMap reduces average energy consumption by 45% and average spike latency by 21%, compared to state-of-the-art techniques.