IEEE Circuits and Systems Magazine - Q1 2018 - 29
Feature
A Memristor-Based
Optimization
Framework
for Artificial
Intelligence
Applications
sijia Liu, yanzhi Wang, makan fardad,
and pramod k. Varshney
Abstract
memristors have recently received significant attention as device-level components for building a novel generation of computing systems.
these devices have many promising features, such as non-volatility, low power consumption, high density, and excellent scalability.
the ability to control and modify biasing voltages at memristor
terminals make them promising candidates to efficiently perform
matrix-vector multiplications and solve systems of linear equations.
in this article, we discuss how networks of memristors arranged in
crossbar arrays can be used for efficiently solving optimization and
machine learning problems. We introduce a new memristor-based
optimization framework that combines the computational merits of
memristor crossbars with the advantages of an operator splitting
method, the alternating direction method of multipliers (admm).
Here, admm helps in splitting a complex optimization problem into
subproblems that involve the solution of systems of linear equations. the strength of this framework is shown by applying it to linear
programming, quadratic programming, and sparse optimization. in
addition to admm, implementation of a customized power iteration
method for eigenvalue/eigenvector computation using memristor
crossbars is discussed. the memristor-based power iteration method can further be applied to principal component analysis. the use
of memristor crossbars yields a significant speed-up in computation,
and thus, we believe, has the potential to advance optimization and
machine learning research in artificial intelligence.
Digital Object Identifier 10.1109/MCAS.2017.2785421
Date of publication: 9 February 2018
first quarter 2018
1531-636X/18©2018ieee
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I. Introduction
emristors, nano-scale devices conceived by Leon
Chua in 1971, have now been physically realized
by scientists and engineers [1], [2]. In contrast to
traditional CMOS technology, memristors can be used
as non-volatile memories for building brain-like learning
machines with memristive synapses [3]. They offer the
ability to construct a dense, continuously programmable, and reasonably accurate cross-point array architecture, which can be used for data-intensive applications
[4]. For example, a memristor crossbar array exhibits a
unique type of parallelism that can be utilized to perform
matrix-vector multiplication and solve systems of linear
m
ieee circuits and systems magazine
29
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