# Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions

arXiv: https://arxiv.org/abs/2203.07006 · HTML: https://mlsanity.com/paper/2203.07006/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2022.

## Built on by (most cited first, 27 of 27)

- [Spike-driven Transformer](https://mlsanity.com/paper/2307.01694/) (2023)
- [Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline](https://mlsanity.com/paper/2304.06793/) (2023)
- [SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms](https://mlsanity.com/paper/2406.03287/) (2024)
- [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)
- [TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents](https://mlsanity.com/paper/2303.01826/) (2023)
- [SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems](https://mlsanity.com/paper/2402.11322/) (2024)
- [To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration](https://mlsanity.com/paper/2306.15749/) (2023)
- [Design Principles for Lifelong Learning AI Accelerators](https://mlsanity.com/paper/2310.04467/) (2023)
- [Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients](https://mlsanity.com/paper/2503.03272/) (2025)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack](https://mlsanity.com/paper/2404.03325/) (2024)
- [RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults](https://mlsanity.com/paper/2304.04041/) (2023)
- [A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design](https://mlsanity.com/paper/2607.09084/) (2026)
- [Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems](https://mlsanity.com/paper/2504.00957/) (2025)
- [Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces](https://mlsanity.com/paper/2312.15889/) (2023)
- [SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGA](https://mlsanity.com/paper/2404.03714/) (2024)
- [Spiking Neural Networks Need High Frequency Information](https://mlsanity.com/paper/2505.18608/) (2025)
- [Neural Network Methods for Radiation Detectors and Imaging](https://mlsanity.com/paper/2311.05726/) (2023)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [NeuroNAS: Enhancing Efficiency of Neuromorphic In-Memory Computing for Intelligent Mobile Agents through Hardware-Aware Spiking Neural Architecture Search](https://mlsanity.com/paper/2407.00641/) (2024)
- [Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA](https://mlsanity.com/paper/2503.06629/) (2025)
- [SpiDR: A Reconfigurable Digital Compute-in-Memory Spiking Neural Network Accelerator for Event-based Perception](https://mlsanity.com/paper/2411.02854/) (2024)
- [Closed-loop control of seizure activity via real-time seizure forecasting by reservoir neuromorphic computing](https://mlsanity.com/paper/2505.02003/) (2025)
- [Spiking Brain Compression: Post-Training Second-order Compression for Spiking Neural Networks](https://mlsanity.com/paper/2506.03996/) (2025)
- [An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface](https://mlsanity.com/paper/2511.22108/) (2025)
- [PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers](https://mlsanity.com/paper/2606.03257/) (2026)
- [PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers](https://mlsanity.com/paper/2606.03428/) (2026)

## Abstract (arXiv)

In this paper, we reviewed Spiking neural network (SNN) integrated circuit designs and analyzed the trends among mixed-signal cores, fully digital cores and large-scale, multi-core designs. Recently reported SNN integrated circuits are compared under three broad categories: (a) Large-scale multi-core designs that have dedicated NOC for spike routing, (b) digital single-core designs and (c) mixed-signal single-core designs. Finally, we finish the paper with some directions for future progress.