SpikingNN is spiking neural network (SNN) simulator written in Julia that targets multiple platforms. The span of appropriate hardware/software combinations for an arbitrary SNNs is quite wide. Emulating an SNN involves simulating a large, interconnected dynamical system — this allows for sparsity in space and time. If a network is dense in both dimensions, a GPU (or multi-GPU) target is most appropriate. If a network uses temporal coding, its activity is sparse over time, so event-driven simulation is well-suited. Targeting these various platforms should not require code changes or complete knowledge of low-level programming requirements. On the other hand, using glue-languages, like Python, to generate low-level code can leave performance on the table and become difficult to extend and debug.

SpikingNN attempts to address these issues by leveraging the rich Julia ecosystem (StructArrays.jl, CuArrays.jl, to name a few packages). These packages allow us to remap the underlying representation of the network to different platforms. This packages provides a framework for building spiking neural networks and simple functions like gpu to map the network to supported targets. It is also designed to be extensible, so that implementing a simple interface allows your model to enjoy the same remapping functionality.

Suported Platforms

Available platforms:

  • Dense CPU
  • Dense single-node GPU

Planned platforms:

  • Sparse CPU
  • Event-driven (sparse in time) CPU
  • Block-sparse in time GPU
  • Distributed CPU/GPU