# Spiking Neural Network inference on FPGAs with hls4ml

arXiv: https://arxiv.org/abs/2606.10008 · HTML: https://mlsanity.com/paper/2606.10008/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 49 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 25 of 25)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017)
- [The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks](https://mlsanity.com/paper/1910.07407/) (2019)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices](https://mlsanity.com/paper/2103.05579/) (2021)
- [Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML](https://mlsanity.com/paper/2003.06308/) (2020)
- [Fast convolutional neural networks on FPGAs with hls4ml](https://mlsanity.com/paper/2101.05108/) (2021)
- [Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge](https://mlsanity.com/paper/2401.01141/) (2024)
- [FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks with Efficient DSP and Memory Optimization](https://mlsanity.com/paper/2301.01905/) (2023)
- [Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml](https://mlsanity.com/paper/2205.07690/) (2022)
- [hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware](https://mlsanity.com/paper/2512.01463/) (2025)
- [A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture](https://mlsanity.com/paper/2404.02248/) (2024)
- [On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments](https://mlsanity.com/paper/2307.11242/) (2023)
- [FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator](https://mlsanity.com/paper/2309.16158/) (2023)
- [Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance](https://mlsanity.com/paper/2203.07516/) (2022)
- [DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs](https://mlsanity.com/paper/2305.05187/) (2023)
- [Neural Network Methods for Radiation Detectors and Imaging](https://mlsanity.com/paper/2311.05726/) (2023)
- [NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning](https://mlsanity.com/paper/2506.14138/) (2025)
- [A Robust, Open-Source Framework for Spiking Neural Networks on Low-End FPGAs](https://mlsanity.com/paper/2507.07284/) (2025)
- [Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data](https://mlsanity.com/paper/1902.00743/) (2019)
- [Unsupervised Particle Tracking with Neuromorphic Computing](https://mlsanity.com/paper/2502.06771/) (2025)
- [Neuromorphic Readout for Hadron Calorimeters](https://mlsanity.com/paper/2502.12693/) (2025)
- [Anomaly detection with spiking neural networks for LHC physics](https://mlsanity.com/paper/2508.00063/) (2025)

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

- [Syn2Logic: End-to-End Neuromorphic Design Automation](https://mlsanity.com/paper/2608.25536/) (2026)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) provide a naturally temporal machine-learning framework. Their neurons maintain an internal state and propagate information through discrete spikes, enabling low-latency temporal inference. Although SNNs are often associated with asynchronous neuromorphic processors, many scientific real-time inference systems rely on conventional synchronous field-programmable gate arrays (FPGAs) and high-level synthesis (HLS) workflows. In this paper we present an extension of hls4ml that enables clock-driven deployment of SNNs trained in pytorch onto FPGA firmware. We demonstrate the workflow using a dense quantised SNN trained on the Heidelberg Spiking Digits dataset where it achieves inference latencies of approximately 34μs. We validate the generated design through software reference comparisons, HLS C simulation, HLS synthesis, export, and Vivado synthesis reports. This work opens up the hls4ml toolkit to neuromorphic computing, allowing streamlined optimisation, synthesis, and deployment of SNN models for real-time inference.