  We present a method to classify objects in video streams using a
brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object
classification is a challenging task where humans still significantly
outperform machine learning algorithms due to their unique capabilities. We
have implemented a system which achieves very promising performance in terms of
recognition accuracy. Unfortunately, conducting more advanced experiments is
very computationally demanding; some of the trials run on a standard CPU may
take as long as several days for 960x540 video streams frames. Therefore we
have decided to accelerate selected parts of the system using OpenCL. In
particular, we seek to determine to what extent porting selected and
computationally demanding parts of a core may speed up calculations.
  The classification accuracy of the system was examined through a series of
experiments and the performance was given in terms of F1 score as a function of
the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$. The
system achieves the highest F1 score of 0.95 and 0.91 for $min\_overlap=4$ and
256 synapses, respectively. We have also conduced a series of experiments with
different hardware setups and measured CPU/GPU acceleration. The best kernel
speed-up of 632x and 207x was reached for 256 synapses and 1024 columns.
However, overall acceleration including transfer time was significantly lower
and amounted to 6.5x and 3.2x for the same setup.
