SNNL: A Programming Language for SNN Development

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, Brian2) show limited expressiveness for neuromorphic applications or poor cross-platform support. This paper 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 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.

Authors

• Qinghui Xing
• Zirun Li
• Ying Li
• Schahram Dustdar
• Xin Du
• Gang Pan
• Shuiguang Deng College of Computer Science and Technology, Zhejiang University, Hangzhou, China

Keywords

• Neurons
• Synapses
• Neuromorphics
• Computational modeling
• Assembly
• Network topology
• Mathematical models
• Membrane potentials
• Computer architecture
• Turing machines
• Programming Language
• Spiking Neural Networks
• Neural Network
• Dynamic Model
• Connectivity Patterns
• Neuronal Dynamics
• Code Generation
• Neuromorphic Computing
• Neuromorphic Systems
• Neuromorphic Hardware
• Transition State
• State Variables
• Neuronal Populations
• Finite Set
• State Machine
• Neuron Model
• Learning Rule
• Postsynaptic Neurons
• Update Rule
• Neural Dynamics
• State Transition Function
• Synaptic Weights
• Spike Signals
• Turing Machine
• Machine Code
• Neuronal Clusters
• Refractory Period
• Instruction Set Architecture
• Equation Solver