# The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing

arXiv: https://arxiv.org/abs/2607.24396 · HTML: https://mlsanity.com/paper/2607.24396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 74 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [Mixture of A Million Experts](https://mlsanity.com/paper/2407.04153/) (2024)
- [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)
- [ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales](https://mlsanity.com/paper/2208.09759/) (2022)
- [Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models](https://mlsanity.com/paper/2404.18508/) (2024)
- [Efficient Video and Audio processing with Loihi 2](https://mlsanity.com/paper/2310.03251/) (2023)
- [Efficient recurrent architectures through activity sparsity and sparse back-propagation through time](https://mlsanity.com/paper/2206.06178/) (2022)
- [SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning](https://mlsanity.com/paper/2401.04491/) (2024)
- [Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing](https://mlsanity.com/paper/2311.14641/) (2023)
- [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)
- [Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System](https://mlsanity.com/paper/1703.01909/) (2017)
- [Language Modeling on a SpiNNaker 2 Neuromorphic Chip](https://mlsanity.com/paper/2312.09084/) (2023)
- [Neuromorphic hardware for sustainable AI data centers](https://mlsanity.com/paper/2402.02521/) (2024)
- [ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers](https://mlsanity.com/paper/2406.05224/) (2024)
- [Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation](https://mlsanity.com/paper/2504.06748/) (2025)
- [Dynamic Power Management for Neuromorphic Many-Core Systems](https://mlsanity.com/paper/1903.08941/) (2019)
- [Real-Time Cortical Simulation on Neuromorphic Hardware](https://mlsanity.com/paper/1909.08665/) (2019)
- [Stochastic Rounding: Algorithms and Hardware Accelerator](https://mlsanity.com/paper/2001.01501/) (2020)
- [Exponentially Faster Language Modelling](https://mlsanity.com/paper/2311.10770/) (2023)
- [Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM](https://mlsanity.com/paper/2304.04842/) (2023)
- [Event-based backpropagation on the neuromorphic platform SpiNNaker2](https://mlsanity.com/paper/2412.15021/) (2024)
- [Lincoln AI Computing Survey (LAICS) Update](https://mlsanity.com/paper/2310.09145/) (2023)
- [An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC](https://mlsanity.com/paper/2507.13736/) (2025)

## Abstract (arXiv)

In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an upcoming alternative to deep networks, taking inspiration from the brain for achieving unprecedented energy efficiency. However, demonstrations of these gains only recently began to grow in complexity and real-world applicability. With SpiNNaker2, we present a chip that bridges the gap between deep networks and neuromorphic computing and allows for flexible exploration of computing approaches that combine both worlds. It features 152 processing elements equipped with an ARM M4F processor and dedicated accelerators, an extended SpiNNaker routing fabric for scalable event-based communication and a range of external interfaces for system integration, including Gbit Ethernet and an LPDDR4 memory interface. We demonstrate performance and efficiency of the SpiNNaker2 chip for neuromorphic and deep network workloads, as well as novel event-based computing approaches. For deep network workloads, the chip achieves up to 4.5 TOPS in high performance mode and up to 2.7 TOPS/W efficiency in high efficiency mode for INT8 workloads. The chip supports spiking neural networks with >150000 neurons and >1.8 billion synaptic events/s when simulated with a 1 ms time step. Its low baseline power of less than 250 mW allows for efficiency even under varying workload conditions, allowing to explore sparse and event-based modes of computation. All this demonstrates the chip's capabilities as a universal hardware platform for scalable brain-inspired computing and its combinations with mainstream deep network approaches.