  Hierarchical Temporal Memory is a new machine learning algorithm intended to
mimic the working principle of neocortex, part of the human brain, which is
responsible for learning, classification, and making predictions. Although many
works illustrate its effectiveness as a software algorithm, hardware design for
HTM remains an open research problem. Hence, this work proposes an architecture
for HTM Spatial Pooler and Temporal Memory with learning mechanism, which
creates a single image for each class based on important and unimportant
features of all images in the training set. In turn, the reduction in the
number of templates within database reduces the memory requirements and
increases the processing speed. Moreover, face recognition analysis indicates
that for a large number of training images, the proposed design provides higher
accuracy results (83.5\%) compared to only Spatial Pooler design presented in
the previous works.
