I will discuss recent experimental results on pattern classification and recognition tasks implemented with memristive  neural networks. The Pt/TiO2-x/Pt memristive devices, which are utilized in both demonstrations, are fabricated with nanoscale e-beam-defined protrusion which localizes the active area during the forming process to ~(20 nm)3 volume and as a result helps in improving device yield. In particular, I will first discuss demonstration of pattern classification task for 3×3 binary images by a single-layer perceptron network implemented with 10 x 2 memristive crossbar circuits in which synaptic weights are realized with memristive devices . The perceptron circuit is trained by ex-situ and in-situ methods to perform binary classification for a set of patterns from an original work of B. Widrow on “memistor” classifiers. Both approaches work successfully despite significant variations in switching behavior of memristive devices as well as half-select and leakage problems in crossbar circuits. Ithen present experimental demonstration of pattern recognition task, in particularly showing 4-bit analog-to-digital conversion (ADC) operation implemented with Hopfield recurrent neural network . A 4-bit ADC is implemented with four inverting amplifiers (neurons), each of which is made with three Si IC operation amplifiers, and a 4´6 memristor crossbar which defines the connectivity among neurons (and bias). In this work the memristors are tuned precisely to the values described in the original Hopfield work using the developed algorithm . Although the considered circuits are simple and hardly practical by itself, the established work presents a proof-of-concept demonstration for highly anticipated memristor-based artificial neural networks and paves the way for extremely dense, high-performance information processing systems.
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