Minibatch Processing in Spiking Neural Networks
This addresses a bottleneck for machine learning practitioners and biological modelers by enabling faster simulation and iteration, though it is incremental as it applies an existing technique (mini-batch processing) to SNNs.
The paper tackled the slow simulation time of spiking neural networks (SNNs) by implementing mini-batch processing in a clock-based simulator, resulting in up to 880X and 24X reductions in wall-clock time for two application domains.
Spiking neural networks (SNNs) are a promising candidate for biologically-inspired and energy efficient computation. However, their simulation is notoriously time consuming, and may be seen as a bottleneck in developing competitive training methods with potential deployment on neuromorphic hardware platforms. To address this issue, we provide an implementation of mini-batch processing applied to clock-based SNN simulation, leading to drastically increased data throughput. To our knowledge, this is the first general-purpose implementation of mini-batch processing in a spiking neural networks simulator, which works with arbitrary neuron and synapse models. We demonstrate nearly constant-time scaling with batch size on a simulation setup (up to GPU memory limits), and showcase the effectiveness of large batch sizes in two SNN application domains, resulting in $\approx$880X and $\approx$24X reductions in wall-clock time respectively. Different parameter reduction techniques are shown to produce different learning outcomes in a simulation of networks trained with spike-timing-dependent plasticity. Machine learning practitioners and biological modelers alike may benefit from the drastically reduced simulation time and increased iteration speed this method enables. Code to reproduce the benchmarks and experimental findings in this paper can be found at https://github.com/djsaunde/snn-minibatch.