Bio-mimetic classification on modern parallel hardware: realizations in NVidia CUDA and OpenMP

Both the brain and modern digital architectures rely on massive parallelismfor efficient solutions to demanding computational tasks, such as pattern recognition. Inthis paper, we implement a parallel classi cation scheme inspired by the insect brain intwo popular parallel computing frameworks, namely as an NVidiarCUDATMimplemen-tation on a TeslaTMdevice and a brute force OpenMPTMparallel implementation on aquad-core CPU. When evaluating the systems on the MNIST data-set of handwrittendigits, we can report that, compared with a standard serial implementation on a singleCPU core, CUDATMimplementations of the bio-inspired classi cation provide a 7-to-11fold speed-up, whereas the OpenMPTMimplementation is 2-to-4 times faster. Our re-sults are a proof of concept that suggests that modern parallel computing architectures andbio-mimetic algorithms are compatible and that the CUDATMsolution on an NVidiarTeslaTMC870 device at the time of writing has a small edge over an OpenMP solutionon a recent quad core processor (3 GHz AMDrPhenomTMII X4 940