Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics

Animal brains still outperform even the most performant machines with significantly
lower speed. Nonetheless, impressive progress has been made in robotics in the
areas of vision, motion- and path planning in the last decades. Brain-inspired Spiking
Neural Networks (SNN) and the parallel hardware necessary to exploit their full potential
have promising features for robotic application. Besides the most obvious platform
for deploying SNN, brain-inspired neuromorphic hardware, Graphical Processing
Units (GPU) are well capable of parallel computing as well. Libraries for generating
CUDA-optimized code, like GeNN and affordable embedded systems make them an
attractive alternative due to their low price and availability. While a few performance tests
exist, there has been a lack of benchmarks targeting robotic applications. We compare
the performance of a neural Wavefront algorithm as a representative of use cases in
robotics on different hardware suitable for running SNN simulations. The SNN used for
this benchmark is modeled in the simulator-independent declarative language PyNN,
which allows using the same model for different simulator backends. Our emphasis is
the comparison between Nest, running on serial CPU, SpiNNaker, as a representative
of neuromorphic hardware, and an implementation in GeNN. Beyond that, we also
investigate the differences of GeNN deployed to different hardware. A comparison
between the different simulators and hardware is performed with regard to total simulation
time, average energy consumption per run, and the length of the resulting path. We
hope that the insights gained about performance details of parallel hardware solutions
contribute to developing more efficient SNN implementations for robotics.