# A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2

arXiv: https://arxiv.org/abs/2510.13757 · HTML: https://mlsanity.com/paper/2510.13757/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks](https://mlsanity.com/paper/1910.07407/) (2019)
- [Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks](https://mlsanity.com/paper/2009.08378/) (2020)
- [Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings](https://mlsanity.com/paper/2306.17670/) (2023)
- [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://mlsanity.com/paper/2201.11063/) (2022)
- [Efficient Video and Audio processing with Loihi 2](https://mlsanity.com/paper/2310.03251/) (2023)
- [Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks](https://mlsanity.com/paper/2212.01232/) (2022)
- [Co-learning synaptic delays, weights and adaptation in spiking neural networks](https://mlsanity.com/paper/2311.16112/) (2023)
- [Balanced Resonate-and-Fire Neurons](https://mlsanity.com/paper/2402.14603/) (2024)
- [Efficient Event-based Delay Learning in Spiking Neural Networks](https://mlsanity.com/paper/2501.07331/) (2025)
- [Learning Delays Through Gradients and Structure: Emergence of Spatiotemporal Patterns in Spiking Neural Networks](https://mlsanity.com/paper/2407.18917/) (2024)
- [Eventprop training for efficient neuromorphic applications](https://mlsanity.com/paper/2503.04341/) (2025)

## Built on by (most cited first, 2 of 2)

- [FeNN-DMA: A RISC-V SoC for SNN acceleration](https://mlsanity.com/paper/2511.00732/) (2025)
- [DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data](https://mlsanity.com/paper/2603.09274/) (2026)

## Abstract (arXiv)

Spiking Neural Networks are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks for edge computing. Neuromorphic computing can significantly reduce energy requirements. Here, we present a complete pipeline: efficient event-based training of SNNs with synaptic delays on GPUs and deployment on Intel's Loihi 2 neuromorphic chip. We evaluate our approach on keyword recognition tasks using the Spiking Heidelberg Digits and Spiking Speech Commands datasets, demonstrating that our algorithm can enhance classification accuracy compared to architectures without delays. Our benchmarking indicates almost no accuracy loss between GPU and Loihi 2 implementations, while classification on Loihi 2 is up to 18x faster and uses 250x less energy than on an NVIDIA Jetson Orin Nano.