  The computational performance of the biological brain has long attracted
significant interest and has led to inspirations in operating principles,
algorithms, and architectures for computing and signal processing. In this
work, we focus on hardware implementation of brain-like learning in a
brain-inspired architecture. We demonstrate, in hardware, that 2-D crossbar
arrays of phase change synaptic devices can achieve associative learning and
perform pattern recognition. Device and array-level studies using an
experimental 10x10 array of phase change synaptic devices have shown that
pattern recognition is robust against synaptic resistance variations and large
variations can be tolerated by increasing the number of training iterations.
Our measurements show that increase in initial variation from 9 % to 60 %
causes required training iterations to increase from 1 to 11.
