165 research outputs found
Memristor models for machine learning
In the quest for alternatives to traditional CMOS, it is being suggested that
digital computing efficiency and power can be improved by matching the
precision to the application. Many applications do not need the high precision
that is being used today. In particular, large gains in area- and power
efficiency could be achieved by dedicated analog realizations of approximate
computing engines. In this work, we explore the use of memristor networks for
analog approximate computation, based on a machine learning framework called
reservoir computing. Most experimental investigations on the dynamics of
memristors focus on their nonvolatile behavior. Hence, the volatility that is
present in the developed technologies is usually unwanted and it is not
included in simulation models. In contrast, in reservoir computing, volatility
is not only desirable but necessary. Therefore, in this work, we propose two
different ways to incorporate it into memristor simulation models. The first is
an extension of Strukov's model and the second is an equivalent Wiener model
approximation. We analyze and compare the dynamical properties of these models
and discuss their implications for the memory and the nonlinear processing
capacity of memristor networks. Our results indicate that device variability,
increasingly causing problems in traditional computer design, is an asset in
the context of reservoir computing. We conclude that, although both models
could lead to useful memristor based reservoir computing systems, their
computational performance will differ. Therefore, experimental modeling
research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio
Mechanistic and Kinetic Analysis of Perovskite Memristors with Buffer Layers: The Case of a Two-Step Set Process
With the increasing demand for artificially intelligent hardware systems for
brain-inspired in-memory and neuromorphic computing, understanding the underlying
mechanisms in the resistive switching of memristor devices is of paramount importance.
Here, we demonstrate a two-step resistive switching set process involving a complex interplay
among mobile halide ions/vacancies (I−/VI
+
) and silver ions (Ag+
) in perovskite-based
memristors with thin undoped buffer layers. The resistive switching involves an initial gradual
increase in current associated with a drift-related halide migration within the perovskite bulk
layer followed by an abrupt resistive switching associated with diffusion of mobile Ag+
conductive filamentary formation. Furthermore, we develop a dynamical model that explains
the characteristic I−V curve that helps to untangle and quantify the switching regimes
consistent with the experimental memristive response. This further insight into the two-step set
process provides another degree of freedom in device design for versatile applications with
varying levels of complexityFunding for open access charge: CRUE-Universitat Jaume
Physical Implementation of a Tunable Memristor-based Chua's Circuit
Nonlinearity is a central feature in demanding computing applications that
aim to deal with tasks such as optimization or classification. Furthermore, the
consensus is that nonlinearity should not be only exploited at the algorithm
level, but also at the physical level by finding devices that incorporate
desired nonlinear features to physically implement energy, area and/or time
efficient computing applications. Chaotic oscillators are one type of system
powered by nonlinearity, which can be used for computing purposes. In this work
we present a physical implementation of a tunable Chua's circuit in which the
nonlinear part is based on a nonvolatile memristive device. Device
characterization and circuit analysis serve as guidelines to design the circuit
and results prove the possibility to tune the circuit oscillatory response by
electrically programming the device.Comment: Accepted by IEEE 48th European Solid State Circuits Conference
(ESSCIRC 2022
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Memristors : a journey from material engineering to beyond Von-Neumann computing
Memristors are a promising building block to the next generation of computing systems. Since 2008, when the physical implementation of a memristor was first postulated, the scientific community has shown a growing interest in this emerging technology. Thus, many other memristive devices have been studied, exploring a large variety of materials and properties. Furthermore, in order to support the design of prac-tical applications, models in different abstract levels have been developed. In fact, a substantial effort has been devoted to the development of memristive based applications, which includes high-density nonvolatile memories, digital and analog circuits, as well as bio-inspired computing. In this context, this paper presents a survey, in hopes of summarizing the highlights of the literature in the last decade
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