Journal ArticleDOI
‘Memristive’ switches enable ‘stateful’ logic operations via material implication
Julien Borghetti,Gregory S. Snider,Philip J. Kuekes,Jianhua Yang,Duncan Stewart,Duncan Stewart,R. Stanley Williams +6 more
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TLDR
Bipolar voltage-actuated switches, a family of nonlinear dynamical memory devices, can execute material implication (IMP), which is a fundamental Boolean logic operation on two variables p and q such that pIMPq is equivalent to (NOTp)ORq.Abstract:
The authors of the International Technology Roadmap for Semiconductors-the industry consensus set of goals established for advancing silicon integrated circuit technology-have challenged the computing research community to find new physical state variables (other than charge or voltage), new devices, and new architectures that offer memory and logic functions beyond those available with standard transistors. Recently, ultra-dense resistive memory arrays built from various two-terminal semiconductor or insulator thin film devices have been demonstrated. Among these, bipolar voltage-actuated switches have been identified as physical realizations of 'memristors' or memristive devices, combining the electrical properties of a memory element and a resistor. Such devices were first hypothesized by Chua in 1971 (ref. 15), and are characterized by one or more state variables that define the resistance of the switch depending upon its voltage history. Here we show that this family of nonlinear dynamical memory devices can also be used for logic operations: we demonstrate that they can execute material implication (IMP), which is a fundamental Boolean logic operation on two variables p and q such that pIMPq is equivalent to (NOTp)ORq. Incorporated within an appropriate circuit, memristive switches can thus perform 'stateful' logic operations for which the same devices serve simultaneously as gates (logic) and latches (memory) that use resistance instead of voltage or charge as the physical state variable.read more
Citations
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Journal ArticleDOI
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis
TL;DR: In this article, a comprehensive neuromemristive crossbar architecture for the spatial pooler and the sparse distributed representation classifier is presented. And the proposed design is benchmarked for image recognition tasks using Modified National Institute of Standards and Technology (MNIST) and Yale faces datasets, and evaluated using different metrics including entropy, sparseness, and noise robustness.
Journal ArticleDOI
Embedded nanoparticle dynamics and their influence on switching behaviour of resistive memory devices
TL;DR: It is found that filaments are composed of metallic clusters and show how filament dynamics link to migration effects of embedded nanoparticles under voltage bias stress, and the formation of metal clusters is promoted by a dynamic interplay of cation mobility and redox rate during switching.
Journal ArticleDOI
Memristive Spiking Neural Networks Trained with Unsupervised STDP
TL;DR: A hardware-friendly architecture and an unsupervised spike-timing dependent plasticity (STDP) learning method for Memristive spiking neural networks (MSNNs) is proposed and results show that the proposed architecture with the learning method achieves a classification accuracy of 94.6%, which outperforms other unsuper supervised SNNs that use time-based encoding schemes.
Book ChapterDOI
Spintronic Logic-in-Memory Paradigms and Implementations
TL;DR: In current big data era, the limitation of data transfer bandwidth (memory wall) between the processor and the memory, and the increase of energy consumption associated with the data transfer (power wall) have become the most urgent problems for conventional von-Neumann architecture.
Journal ArticleDOI
Novel implementation of memristive systems for data encryption and obfuscation
Nan Du,Niveditha Manjunath,Yao Shuai,Danilo Bürger,Ilona Skorupa,Rene Schuffny,Christian Mayr,Dimitri Basov,Massimiliano Di Ventra,Oliver G. Schmidt,Heidemarie Schmidt +10 more
TL;DR: It is found that a BiFeO3 memristor in high and low resistance state can be used to generate two clearly distinguishable sets of second and higher harmonics as recently predicted theoretically.
References
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Journal ArticleDOI
The missing memristor found
TL;DR: It is shown, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage.
Journal ArticleDOI
Memristor-The missing circuit element
TL;DR: In this article, the memristor is introduced as the fourth basic circuit element and an electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell's equations is presented.
Journal ArticleDOI
Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
Journal ArticleDOI
Memristive switching mechanism for metal/oxide/metal nanodevices.
Jianhua Yang,Matthew D. Pickett,Xuema Li,Douglas A. A. Ohlberg,Duncan Stewart,R. Stanley Williams +5 more
TL;DR: Experimental evidence is provided to support this general model of memristive electrical switching in oxide systems, and micro- and nanoscale TiO2 junction devices with platinum electrodes that exhibit fast bipolar nonvolatile switching are built.
Journal ArticleDOI
Memristive devices and systems
Leon O. Chua,Sung-Mo Kang +1 more
TL;DR: In this article, a broad generalization of memristors to an interesting class of nonlinear dynamical systems called memristive systems is introduced, which are unconventional in the sense that while they behave like resistive devices, they can be endowed with a rather exotic variety of dynamic characteristics.