[2607.24396] The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing

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."/>

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." />

Skip to main content

Search arXiv

Press Enter to search · Advanced search

-->

Computer Science > Emerging Technologies

arXiv:2607.24396 (cs)

[Submitted on 27 Jul 2026]

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

Authors:Stefan Scholze, Johannes Partzsch, Sebastian Höppner, Florian Kelber, Andreas Dixius, Marco Stolba, Sirine Arfa, Marc Berthel, Georg Ellguth, Jim Garside, Hector A. Gonzalez, Stephan Hartmann, Thomas Kiel-Hocker, Dongwei Hu, Matthias Jobst, Khaleelulla Khan Nazeer, Tim Langer, Chen Liu, Gengting Liu, Matthias Lohrmann, Mantas Mikaitis, Felix Neumärker, Amirhossein Rostami, Stefan Schiefer, Tilo Schubert, Delong Shang, Bernhard Vogginger, Yexin Yan, Steve Furber, Christian Mayr<br>View a PDF of the paper titled The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing, by Stefan Scholze and 29 other authors

View PDF<br>HTML (experimental)

Abstract: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.

Comments:<br>19 pages, 13 figures

Subjects:

Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as:<br>arXiv:2607.24396 [cs.ET]

(or<br>arXiv:2607.24396v1 [cs.ET] for this version)

https://doi.org/10.48550/arXiv.2607.24396

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Journal reference:<br>IEEE Open Journal of Circuits and Systems 2026

Related DOI:

https://doi.org/10.1109/OJCAS.2026.3714974

Focus to learn more

DOI(s) linking to related resources

Submission history<br>From: Bernhard Vogginger [view email]<br>[v1]<br>Mon, 27 Jul 2026 13:13:02 UTC (8,835 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing, by Stefan Scholze and 29 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.ET

next >

new<br>recent<br>| 2026-07

Change to browse by:

cs<br>cs.AI<br>cs.AR<br>cs.DC

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer...