# The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing

arXiv: https://arxiv.org/abs/2103.08392 · HTML: https://mlsanity.com/paper/2103.08392/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2021.

## Built on by (most cited first, 14 of 14)

- [Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips](https://mlsanity.com/paper/2404.03663/) (2024)
- [SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions](https://mlsanity.com/paper/2204.10687/) (2022)
- [Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Time](https://mlsanity.com/paper/2201.07210/) (2021)
- [Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM](https://mlsanity.com/paper/2304.04842/) (2023)
- [Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localization](https://mlsanity.com/paper/2512.19365/) (2025)
- [SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression](https://mlsanity.com/paper/2604.18610/) (2026)
- [A Multi-Threading Kernel for Enabling Neuromorphic Edge Applications](https://mlsanity.com/paper/2510.17745/) (2025)
- [Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computing](https://mlsanity.com/paper/2603.10246/) (2026)
- [Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net](https://mlsanity.com/paper/2411.14585/) (2024)
- [Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems](https://mlsanity.com/paper/2412.05126/) (2024)
- [Full Feature Spiking Neural Network Simulation on Micro-Controllers for Neuromorphic Applications at the Edge](https://mlsanity.com/paper/2604.16474/) (2026)
- [Spike-driven Large Language Model](https://mlsanity.com/paper/2604.16475/) (2026)
- [EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures](https://mlsanity.com/paper/2604.27004/) (2026)
- [Syn2Logic: End-to-End Neuromorphic Design Automation](https://mlsanity.com/paper/2608.25536/) (2026)

## 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.