# Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review

arXiv: https://arxiv.org/abs/2202.08897 · HTML: https://mlsanity.com/paper/2202.08897/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2022.

## Built on by (most cited first, 15 of 15)

- [DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor](https://mlsanity.com/paper/2310.00564/) (2023)
- [Neural Brain: A Neuroscience-inspired Framework for Embodied Agents](https://mlsanity.com/paper/2505.07634/) (2025)
- [Spiking SiamFC++: Deep Spiking Neural Network for Object Tracking](https://mlsanity.com/paper/2209.12010/) (2022)
- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16236/) (2023)
- [Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training](https://mlsanity.com/paper/2205.13585/) (2022)
- [A Faster Approach to Spiking Deep Convolutional Neural Networks](https://mlsanity.com/paper/2210.17442/) (2022)
- [NEUROSEC: FPGA-Based Neuromorphic Audio Security](https://mlsanity.com/paper/2401.12055/) (2024)
- [What is Cognitive Computing? An Architecture and State of The Art](https://mlsanity.com/paper/2301.00882/) (2023)
- [Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units](https://mlsanity.com/paper/2403.11563/) (2024)
- [HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physics](https://mlsanity.com/paper/2412.17571/) (2024)
- [STEMS: Spatial-Temporal Mapping For Spiking Neural Networks](https://mlsanity.com/paper/2502.03287/) (2025)
- [Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms](https://mlsanity.com/paper/2505.16362/) (2025)
- [Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2507.18139/) (2025)
- [Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics](https://mlsanity.com/paper/2603.13880/) (2026)

## Abstract (arXiv)

Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology fronts, programming such systems to admit and execute a machine learning application is becoming increasingly challenging. Additionally, neuromorphic systems are required to guarantee real-time performance, consume lower energy, and provide tolerance to logic and memory failures. Consequently, there is a clear need for system software frameworks that can implement machine learning applications on current and emerging neuromorphic systems, and simultaneously address performance, energy, and reliability. Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design. We highlight challenges and opportunities that the future holds in the area of system software technology for neuromorphic computing.