# Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning

arXiv: https://arxiv.org/abs/2312.17582 · HTML: https://mlsanity.com/paper/2312.17582/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2023.

## Built on by (most cited first, 22 of 22)

- [SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems](https://mlsanity.com/paper/2402.11322/) (2024)
- [Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning](https://mlsanity.com/paper/2406.01072/) (2024)
- [Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients](https://mlsanity.com/paper/2503.03272/) (2025)
- [Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions](https://mlsanity.com/paper/2409.02111/) (2024)
- [Hybrid Spiking Vision Transformer for Object Detection with Event Cameras](https://mlsanity.com/paper/2505.07715/) (2025)
- [Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning](https://mlsanity.com/paper/2505.14535/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [SpikeFit: Towards Optimal Deployment of Spiking Networks on Neuromorphic Hardware](https://mlsanity.com/paper/2510.15542/) (2025)
- [STOP: Spatiotemporal Orthogonal Propagation for Weight-Threshold-Leakage Synergistic Training of Deep Spiking Neural Networks](https://mlsanity.com/paper/2411.11082/) (2024)
- [Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks](https://mlsanity.com/paper/2505.05375/) (2025)
- [Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning](https://mlsanity.com/paper/2510.04098/) (2025)
- [Sparse by Rule: Probability-Based N:M Pruning for Spiking Neural Networks](https://mlsanity.com/paper/2511.12097/) (2025)
- [Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neurons](https://mlsanity.com/paper/2502.14487/) (2025)
- [Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning](https://mlsanity.com/paper/2505.07921/) (2025)
- [DarwinWafer: A Wafer-Scale Neuromorphic Chip](https://mlsanity.com/paper/2509.16213/) (2025)
- [A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks](https://mlsanity.com/paper/2510.20683/) (2025)
- [Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks](https://mlsanity.com/paper/2512.22522/) (2025)
- [Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision](https://mlsanity.com/paper/2601.10742/) (2026)
- [Robust Spiking Neural Networks Against Adversarial Attacks](https://mlsanity.com/paper/2602.20548/) (2026)
- [Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks](https://mlsanity.com/paper/2603.11676/) (2026)
- [IncreRTL: Traceability-Guided Incremental RTL Generation under Requirement Evolution](https://mlsanity.com/paper/2603.25769/) (2026)
- [ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing](https://mlsanity.com/paper/2605.20802/) (2026)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture(ISA), which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind in neuron scale. The experimental results showed that code density was improved up to 28.3x in Darwin3, and neuron core fan-in and fan-out were improved up to 4096x and 3072x by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8X and 200.8X when mapping convolutional spiking neural networks (CSNN) onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.