  Open-source simulation tools play a crucial role for neuromorphic application
engineers and hardware architects to investigate performance bottlenecks and
explore design optimizations before committing to silicon. Reconfigurable
Architecture for Neuromorphic Computing (RANC) is one such tool that offers
ability to execute pre-trained Spiking Neural Network (SNN) models within a
unified ecosystem through both software-based simulation and FPGA-based
emulation. RANC has been utilized by the community with its flexible and highly
parameterized design to study implementation bottlenecks, tune architectural
parameters or modify neuron behavior based on application insights and study
the trade space on hardware performance and network accuracy. In designing
architectures for use in neuromorphic computing, there are an incredibly large
number of configuration parameters such as number and precision of weights per
neuron, neuron and axon counts per core, network topology, and neuron behavior.
To accelerate such studies and provide users with a streamlined productive
design space exploration, in this paper we introduce the GPU-based
implementation of RANC. We summarize our parallelization approach and quantify
the speedup gains achieved with GPU-based tick-accurate simulations across
various use cases. We demonstrate up to 780 times speedup compared to serial
version of the RANC simulator based on a 512 neuromorphic core MNIST inference
application. We believe that the RANC ecosystem now provides a much more
feasible avenue in the research of exploring different optimizations for
accelerating SNNs and performing richer studies by enabling rapid convergence
to optimized neuromorphic architectures.
