# SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning

arXiv: https://arxiv.org/abs/2401.04491 · HTML: https://mlsanity.com/paper/2401.04491/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Retentive Network: A Successor to Transformer for Large Language Models](https://mlsanity.com/paper/2307.08621/) (2023)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [The Hardware Lottery](https://mlsanity.com/paper/2009.06489/) (2020)
- [Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks](https://mlsanity.com/paper/2009.08378/) (2020)
- [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://mlsanity.com/paper/2201.11063/) (2022)
- [Efficient recurrent architectures through activity sparsity and sparse back-propagation through time](https://mlsanity.com/paper/2206.06178/) (2022)
- [Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing](https://mlsanity.com/paper/2311.14641/) (2023)
- [Dynamic Power Management for Neuromorphic Many-Core Systems](https://mlsanity.com/paper/1903.08941/) (2019)
- [SpiNNTools: The Execution Engine for the SpiNNaker Platform](https://mlsanity.com/paper/1810.06835/) (2018)
- [Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype](https://mlsanity.com/paper/1903.08500/) (2019)

## Built on by (most cited first, 25 of 25)

- [An efficient probabilistic hardware architecture for diffusion-like models](https://mlsanity.com/paper/2510.23972/) (2025)
- [Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation](https://mlsanity.com/paper/2504.06748/) (2025)
- [Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras](https://mlsanity.com/paper/2411.03022/) (2024)
- [Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective](https://mlsanity.com/paper/2504.05341/) (2025)
- [FeNN: A RISC-V vector processor for Spiking Neural Network acceleration](https://mlsanity.com/paper/2506.11760/) (2025)
- [Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture](https://mlsanity.com/paper/2606.00288/) (2026)
- [Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware](https://mlsanity.com/paper/2412.04008/) (2024)
- [SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks](https://mlsanity.com/paper/2504.02298/) (2025)
- [An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC](https://mlsanity.com/paper/2507.13736/) (2025)
- [Random Spiking Neural Networks are Stable and Spectrally Simple](https://mlsanity.com/paper/2511.00904/) (2025)
- [BiKA: Kolmogorov-Arnold-Network-inspired Ultra Lightweight Neural Network Hardware Accelerator](https://mlsanity.com/paper/2602.23455/) (2026)
- [GPU-RANC: A CUDA Accelerated Simulation Framework for Neuromorphic Architectures](https://mlsanity.com/paper/2404.16208/) (2024)
- [TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration](https://mlsanity.com/paper/2410.15854/) (2024)
- [Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neurons](https://mlsanity.com/paper/2502.14487/) (2025)
- [Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing](https://mlsanity.com/paper/2506.20782/) (2025)
- [Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform](https://mlsanity.com/paper/2507.23562/) (2025)
- [Event-driven Robust Fitting on Neuromorphic Hardware](https://mlsanity.com/paper/2508.09466/) (2025)
- [Synaptic bundle theory for spike-driven sensor-motor system: More than eight independent synaptic bundles collapse reward-STDP learning](https://mlsanity.com/paper/2508.14492/) (2025)
- [FeNN-DMA: A RISC-V SoC for SNN acceleration](https://mlsanity.com/paper/2511.00732/) (2025)
- [Proof-of-Spiking-Neurons(PoSN): Neuromorphic Consensus for Next-Generation Blockchains](https://mlsanity.com/paper/2511.02868/) (2025)
- [Learning Long-Range Dependencies with Temporal Predictive Coding](https://mlsanity.com/paper/2602.18131/) (2026)
- [Joint sparse coding and temporal dynamics support context reconfiguration](https://mlsanity.com/paper/2605.10178/) (2026)
- [ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing](https://mlsanity.com/paper/2605.20802/) (2026)
- [Robust PnP on a Neuromorphic Processor for Object Pose Estimation](https://mlsanity.com/paper/2607.16834/) (2026)
- [The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing](https://mlsanity.com/paper/2607.24396/) (2026)

## Abstract (arXiv)

The joint progress of artificial neural networks (ANNs) and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research. This development is accompanied by a rapid growth of the required computational demands for larger models and more data. Concurrently, emerging properties of foundation models such as in-context learning drive new opportunities for machine learning applications. However, the computational cost of such applications is a limiting factor of the technology in data centers, and more importantly in mobile devices and edge systems. To mediate the energy footprint and non-trivial latency of contemporary systems, neuromorphic computing systems deeply integrate computational principles of neurobiological systems by leveraging low-power analog and digital technologies. SpiNNaker2 is a digital neuromorphic chip developed for scalable machine learning. The event-based and asynchronous design of SpiNNaker2 allows the composition of large-scale systems involving thousands of chips. This work features the operating principles of SpiNNaker2 systems, outlining the prototype of novel machine learning applications. These applications range from ANNs over bio-inspired spiking neural networks to generalized event-based neural networks. With the successful development and deployment of SpiNNaker2, we aim to facilitate the advancement of event-based and asynchronous algorithms for future generations of machine learning systems.