Memristor-based perceptron classifier: Increasing complexity and coping with imperfect hardware

Abstract

We experimentally demonstrate classification of 4×4 binary images into 4 classes, using a 3-layer mixed-signal neuromorphic network (“MLP perceptron”), based on two passive 20×20 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 passive (0T1R) memristor classifier. Moreover, the inference operation of this classifier is performed entirely in the integrated hardware. To deal with larger crossbar arrays, we have developed a semiautomatic 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.