# DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor

arXiv: https://arxiv.org/abs/2310.00564 · HTML: https://mlsanity.com/paper/2310.00564/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 77 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://mlsanity.com/paper/2201.11063/) (2022)
- [Processing Data Where It Makes Sense: Enabling In-Memory Computation](https://mlsanity.com/paper/1903.03988/) (2019)
- [Neuromorphic electronic circuits for building autonomous cognitive systems](https://mlsanity.com/paper/1403.6428/) (2014)
- [Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review](https://mlsanity.com/paper/2202.08897/) (2022)
- [An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG](https://mlsanity.com/paper/2009.11245/) (2020)
- [The importance of space and time in neuromorphic cognitive agents](https://mlsanity.com/paper/1902.09791/) (2019)
- [Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence](https://mlsanity.com/paper/2006.14270/) (2020)
- [Verification and Design Methods for the BrainScaleS Neuromorphic Hardware System](https://mlsanity.com/paper/2003.11455/) (2020)
- [Vector Symbolic Finite State Machines in Attractor Neural Networks](https://mlsanity.com/paper/2212.01196/) (2022)
- [An event-based architecture for solving constraint satisfaction problems](https://mlsanity.com/paper/1505.01139/) (2015)

## Built on by (most cited first, 21 of 21)

- [Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge](https://mlsanity.com/paper/2401.01141/) (2024)
- [DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware](https://mlsanity.com/paper/2404.19165/) (2024)
- [A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing](https://mlsanity.com/paper/2409.15022/) (2024)
- [The Role of Temporal Hierarchy in Spiking Neural Networks](https://mlsanity.com/paper/2407.18838/) (2024)
- [Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness](https://mlsanity.com/paper/2503.17394/) (2025)
- [A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks](https://mlsanity.com/paper/2602.13261/) (2026)
- [Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks](https://mlsanity.com/paper/2603.13283/) (2026)
- [Spike-based alignment learning solves the weight transport problem](https://mlsanity.com/paper/2503.02642/) (2025)
- [Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation](https://mlsanity.com/paper/2503.21795/) (2025)
- [A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures](https://mlsanity.com/paper/2409.14918/) (2024)
- [Network Representation Learning for Biophysical Neural Network Analysis](https://mlsanity.com/paper/2410.11503/) (2024)
- [TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration](https://mlsanity.com/paper/2410.15854/) (2024)
- [Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net](https://mlsanity.com/paper/2411.14585/) (2024)
- [Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2507.18139/) (2025)
- [Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks](https://mlsanity.com/paper/2509.10077/) (2025)
- [ASecond-Order SpikingSSM for Wearables](https://mlsanity.com/paper/2510.14386/) (2025)
- [FeNN-DMA: A RISC-V SoC for SNN acceleration](https://mlsanity.com/paper/2511.00732/) (2025)
- [Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization](https://mlsanity.com/paper/2607.14672/) (2026)
- [BASC : Behavior-Aligned Quantization and Pruning for Low-Bit Spiking Neural Networks](https://mlsanity.com/paper/2608.19239/) (2026)
- [Real-Time Neuromorphic Spectrum Intelligence Simulator](https://mlsanity.com/paper/2609.00585/) (2026)
- [Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networks](https://mlsanity.com/paper/2609.37047/) (2026)

## Abstract (arXiv)

With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract relevant information using the smallest possible energy budgets. A promising approach for implementing always-on processing of sensory signals that supports on-demand, sparse, and edge-computing is to take inspiration from biological nervous system. Following this approach, we present a brain-inspired platform for prototyping real-time event-based Spiking Neural Networks (SNNs). The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The analog circuits that implement such primitives are paired with a low latency asynchronous digital circuits for routing and mapping events. This asynchronous infrastructure enables the definition of different network architectures, and provides direct event-based interfaces to convert and encode data from event-based and continuous-signal sensors. Here we describe the overall system architecture, we characterize the mixed signal analog-digital circuits that emulate neural dynamics, demonstrate their features with experimental measurements, and present a low- and high-level software ecosystem that can be used for configuring the system. The flexibility to emulate different biologically plausible neural networks, and the chip's ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.