Neuromorphic Computing with Memristive Circuits
Monday, November 11, 2013 - 4:00pm - 4:50pm
KEC 1001
Dmitri Strukov
Assistant Professor
Department of Electrical and Computer Engineering
University of California, Santa Barbara
Abstract
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 3x3 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 "memistor" classifiers. Both
approaches work successfully despite significant variations in switching behavior of
memristive devices as well as half-select and leakag!
e 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 4'6 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] J.J. Yang, D.B. Strukov and D.R. Stewart, Nature Nanotechnology 8 (2013)
13-24.
[2] F.Alibart, E. Zamanidoost, D.B. Strukov, Nature Communications, (2013) 25th
June.
[3] L. Gao, F. Merrikh-Bayat, F. Alibart, X. Guo, B.D. Hoskins, K.-T. Cheng,
and D.B. Strukov, in: Proc. NanoArch (2013).
[4] F. Alibart, L. Gao, B. Hoskins, D. B. Strukov, Nanotechnology 23 (2012)
075201.
Speaker Biography:
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.
_______________________________________________
Colloquium mailing list
[email protected]
https://secure.engr.oregonstate.edu/mailman/listinfo/colloquium
