SNNL: A Programming Language for Spiking Neural Network Development

doi:10.1109/mis.2025.3636128

SNNL: A Programming Language for Spiking Neural Network Development

2025-11-25

0

PRE

AI

DOI：10.1109/mis.2025.3636128

Original

Original request for help

Save

Abstract

En 中文

Spiking neural networks (SNNs) are gaining attention for biological plausibility and energy efficiency. Advances in neuromorphic systems—integrating hardware and software tools—accelerate SNN implementation. Yet, deploying SNNs on such platforms remains challenging due to model complexity and system heterogeneity, requiring flexible frameworks. Existing tools (e.g., PyNN and Brian2) show limited expressiveness for neuromorphic applications or poor cross-platform support. This article proposes SNNL, a flexible, domain-specific language for SNN development and deployment on neuromorphic hardware. SNNL decouples neuronal dynamics modeling from network topology specification: equation-based representations handle diverse neuron/synapse models, while hierarchical constructs define complex connectivity patterns. We present a Darwin3-targeted compiler with efficient code generation. Evaluations confirm that SNNL achieves precise neuronal dynamic descriptions and flexible network configurations. This work bridges algorithm-hardware gaps in neuromorphic computing by enhancing programmability. Experimental results have demonstrated the feasibility of SNNL in developing SNNs for neuromorphic systems.

Keywords:

Spiking neural networks

Computer languages

Neuromorphics

Hardware

Performance evaluation

Domain specific languages

Network architecture

SNNL: A Programming Language for Spiking Neural Network Development

Abstract

Journal

Organization