Faster and Simpler SNN Simulation with Work Queues
This work addresses efficiency and usability issues for researchers and developers in computational neuroscience and AI, though it appears incremental as it builds on existing simulation paradigms.
The paper tackled the problem of slow and complex Spiking Neural Network simulation by introducing a clock-driven simulator that is up to 3x faster than state-of-the-art methods while being more general and easier to use.
We present a clock-driven Spiking Neural Network simulator which is up to 3x faster than the state of the art while, at the same time, being more general and requiring less programming effort on both the user's and maintainer's side. This is made possible by designing our pipeline around "work queues" which act as interfaces between stages and greatly reduce implementation complexity. We evaluate our work using three well-established SNN models on a series of benchmarks.