  We experimentally demonstrate classification of 4x4 binary images into 4
classes, using a 3-layer mixed-signal neuromorphic network ("MLP perceptron"),
based on two passive 20x20 memristive crossbar arrays, board-integrated with
discrete CMOS components. The network features 10 hidden-layer and 4
output-layer analog CMOS neurons and 428 metal-oxide memristors, i.e. is almost
an order of magnitude more complex than any previously reported functional
memristor circuit. Moreover, the inference operation of this classifier is
performed entirely in the integrated hardware. To deal with larger crossbar
arrays, we have developed a semi-automatic approach to their forming and
testing, and compared several memristor training schemes for coping with
imperfect behavior of these devices, as well as with variability of analog CMOS
neurons. The effectiveness of the proposed schemes for defect and variation
tolerance was verified experimentally using the implemented network and,
additionally, by modeling the operation of a larger network, with 300
hidden-layer neurons, on the MNIST benchmark. Finally, we propose a simple
modification of the implemented memristor-based vector-by-matrix multiplier to
allow its operation in a wider temperature range.
