Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics.

ABSTRACT: 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 t

SUBMITTER: Steffen L 

PROVIDER: S-EPMC8275645 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature