# Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware

arXiv: https://arxiv.org/abs/2407.08704 · HTML: https://mlsanity.com/paper/2407.08704/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Object Detection with Spiking Neural Networks on Automotive Event Data](https://mlsanity.com/paper/2205.04339/) (2022)
- [Spiking Neural Networks and Online Learning: An Overview and Perspectives](https://mlsanity.com/paper/1908.08019/) (2019)
- [Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision](https://mlsanity.com/paper/2112.03423/) (2021)
- [Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware](https://mlsanity.com/paper/2207.12559/) (2022)
- [Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU](https://mlsanity.com/paper/2305.15422/) (2023)
- [Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators](https://mlsanity.com/paper/2403.08792/) (2024)
- [Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware](https://mlsanity.com/paper/2210.05006/) (2022)
- [Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification](https://mlsanity.com/paper/2102.10592/) (2021)

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

- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [BLADE: ReliaBle Dynamic Hardware-Aware SNN-ANN Boundary SeLection for Event-BAseD Object DEtection](https://mlsanity.com/paper/2609.17562/) (2026)

## Abstract (arXiv)

This paper explores the synergistic potential of neuromorphic and edge computing to create a versatile machine learning (ML) system tailored for processing data captured by dynamic vision sensors. We construct and train hybrid models, blending spiking neural networks (SNNs) and artificial neural networks (ANNs) using PyTorch and Lava frameworks. Our hybrid architecture integrates an SNN for temporal feature extraction and an ANN for classification. We delve into the challenges of deploying such hybrid structures on hardware. Specifically, we deploy individual components on Intel's Neuromorphic Processor Loihi (for SNN) and Jetson Nano (for ANN). We also propose an accumulator circuit to transfer data from the spiking to the non-spiking domain. Furthermore, we conduct comprehensive performance analyses of hybrid SNN-ANN models on a heterogeneous system of neuromorphic and edge AI hardware, evaluating accuracy, latency, power, and energy consumption. Our findings demonstrate that the hybrid spiking networks surpass the baseline ANN model across all metrics and outperform the baseline SNN model in accuracy and latency.