# Spiking Neural Networks Hardware Implementations and Challenges: a Survey

arXiv: https://arxiv.org/abs/2005.01467 · HTML: https://mlsanity.com/paper/2005.01467/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 18 of 18)

- [Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence](https://mlsanity.com/paper/2106.01288/) (2021)
- [Ternary Spike-based Neuromorphic Signal Processing System](https://mlsanity.com/paper/2407.05310/) (2024)
- [Noise-Robust Deep Spiking Neural Networks with Temporal Information](https://mlsanity.com/paper/2104.11169/) (2021)
- [DSP-Packing: Squeezing Low-precision Arithmetic into FPGA DSP Blocks](https://mlsanity.com/paper/2203.11028/) (2022)
- [A Time-to-first-spike Coding and Conversion Aware Training for Energy-Efficient Deep Spiking Neural Network Processor Design](https://mlsanity.com/paper/2208.04494/) (2022)
- [SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions](https://mlsanity.com/paper/2204.10687/) (2022)
- [Collective control of modular soft robots via embodied Spiking Neural Cellular Automata](https://mlsanity.com/paper/2204.02099/) (2022)
- [Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function](https://mlsanity.com/paper/2305.19295/) (2023)
- [SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGA](https://mlsanity.com/paper/2404.03714/) (2024)
- [Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model](https://mlsanity.com/paper/2410.15689/) (2024)
- [Neural Network Methods for Radiation Detectors and Imaging](https://mlsanity.com/paper/2311.05726/) (2023)
- [KirchhoffNet: A Scalable Ultra Fast Analog Neural Network](https://mlsanity.com/paper/2310.15872/) (2023)
- [Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies](https://mlsanity.com/paper/2411.04354/) (2024)
- [Marine Vehicles Localization Using Grid Cells for Path Integration](https://mlsanity.com/paper/2107.13461/) (2021)
- [NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector](https://mlsanity.com/paper/2112.06102/) (2021)
- [Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review](https://mlsanity.com/paper/2312.07213/) (2023)
- [A Neuromorphic Approach to Obstacle Avoidance in Robot Manipulation](https://mlsanity.com/paper/2404.05858/) (2024)
- [Surrogates, Spikes, and Sparsity: Performance Analysis and Characterization of SNN Hyperparameters on Hardware](https://mlsanity.com/paper/2603.24891/) (2026)

## Abstract (arXiv)

Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at bringing closer the memory and the computational elements to efficiently evaluate machine-learning algorithms. Recently, Spiking Neural Networks, a generation of cognitive algorithms employing computational primitives mimicking neuron and synapse operational principles, have become an important part of deep learning. They are expected to improve the computational performance and efficiency of neural networks, but are best suited for hardware able to support their temporal dynamics. In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration from model selection to training mechanisms. The scope of existing solutions is extensive; we thus present the general framework and study on a case-by-case basis the relevant particularities. We describe the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level and discuss their related advantages and challenges.