I will discuss recent experimental results on pattern classification and recognition tasks implemented with memristive [1] 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脳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 [2]. 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 鈥渕emistor鈥 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 [3]. 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麓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 [4]. 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.
References
[1]聽聽聽聽聽聽聽聽聽聽 J.J. Yang, D.B. Strukov and D.R. Stewart, Nature Nanotechnology 8 (2013) 13-24.
[2] 聽聽聽聽聽聽聽聽聽 F.Alibart, E. Zamanidoost, D.B. Strukov, Nature Communications, (2013) 25th June.
[3] 聽聽聽聽聽聽聽聽聽 L. Gao, F. Merrikh-Bayat, F. Alibart, X. Guo, B.D. Hoskins, K.-T. Cheng, and D.B. Strukov, in: Proc. NanoArch (2013).
[4]聽聽聽聽聽聽聽聽聽聽 F. Alibart, L. Gao, B. Hoskins, D. B. Strukov, Nanotechnology 23 (2012) 075201.
Dmitri Strukov is an assistant professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB). Dr. Strukov received MS in applied physics and mathematics from the Moscow Institute of Physics and Technology in 1999 and a PhD in electrical engineering from Stony Brook University in New York in 2006. In general, he is broadly interested in a physical implementation of computation, including device physics, circuit design, and high-level architecture, with emphasis on emerging device technologies. In particular, his main focus now is on various aspects of reconfigurable hybrid nanoelectronic systems, utilizing novel resistive switching ("memristive") device, for applications in digital memories, programmable logic, and neuromorphic networks. Prior to joining UCSB he worked as a postdoctoral associate at Hewlett Packard Laboratories from 2007 to 2009.