The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing
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Abstract (arXiv)
This paper introduces the processing element architecture of the second generation SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive body biasing for near-threshold operation, and dynamic voltage-and-frequency scaling driven by spiking activity. On system level, processing is centered around an ARM M4 core, similar to the processor-centric architecture of the first generation SpiNNaker. To speed operation of subtasks, we have added accelerators for numerical operations of both spiking (SNN) and rate based (deep) neural networks (DNN). PEs communicate via a dedicated, custom-designed network-on-chip. We present three benchmarks showing operation of the whole processor element on SNN, DNN and hybrid SNN/DNN networks.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 3; 2025: 2; 2026: 5.
12 papers.
- 2024Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chipscited by 97
- 2021Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Timecited by 3
- 2023Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVMcited by 3
- 2025Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localizationcited by 2
- 2026SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compressioncited by 2
- 2025A Multi-Threading Kernel for Enabling Neuromorphic Edge Applicationscited by 1
- 2026Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computingcited by 1
- 2024Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Netcited by 0
- 2024Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systemscited by 0
- 2026Full Feature Spiking Neural Network Simulation on Micro-Controllers for Neuromorphic Applications at the Edgecited by 0
- 2026EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architecturescited by 0
- 2026Syn2Logic: End-to-End Neuromorphic Design Automationcited by 0
- 2026Syn2Logic: End-to-End Neuromorphic Design Automation26 Aug 2026
- 2026EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures29 Apr 2026
- 2026SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression13 Apr 2026
- 2026Full Feature Spiking Neural Network Simulation on Micro-Controllers for Neuromorphic Applications at the Edge11 Apr 2026
- 2026Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computing10 Mar 2026
- 2025Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localization22 Dec 2025
- 2025A Multi-Threading Kernel for Enabling Neuromorphic Edge Applications20 Oct 2025
- 2024Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems6 Dec 2024
- 2024Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net21 Nov 2024
- 2024Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips15 Feb 2024
- 2023Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM10 Apr 2023
- 2021Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Time13 Dec 2021