Memistor

About: Memistor is a research topic. Over the lifetime, 608 publications have been published within this topic receiving 34905 citations.

Combined Compute and Storage: Configurable Memristor Arrays to Accelerate Search.

Abstract: Emerging technologies present opportunities for system designers to meet the challenges presented by competing trends of big data analytics and limitations on CMOS scaling. Specifically, memristors are an emerging high-density technology where the individual memristors can be used as storage or to perform computation. The voltage applied across a memristor determines its behavior (storage vs. compute), which enables a configurable memristor substrate that can embed computation with storage. This paper explores accelerating point and range search queries as instances of the more general configurable combined compute and storage capabilities of memristor arrays. We first present MemCAM, a configurable memristor-based content addressable memory for the cases when fast, infrequent searches over large datasets are required. For frequent searches, memristor lifetime becomes a concern. To increase memristor array lifetime we introduce hybrid data structures that combine trees with MemCAM using conventional CMOS processor/cache hierarchies for the upper levels of the tree and configurable memristor technologies for lower levels. We use SPICE to analyze energy consumption and access time of memristors and use analytic models to evaluate the performance of configurable hybrid data structures. The results show that with acceptable energy consumption our configurable hybrid data structures improve performance of search intensive applications and achieve lifetime in years or decades under continuous queries. Furthermore, the configurability of memristor arrays and the proposed data structures provide opportunities to tune the trade- off between performance and lifetime and the data structures can be easily adapted to future memristors or other technologies with improved endurance.

Training Memristors for Reliable Computing.

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Multimodal memristor memory

Abstract: A multimodal memristor memory provides selectable or reconfigurable operation in a plurality of operational modes of a memristor. The multimodal memristor memory includes a memristor having a plurality of operational modes. The multimodal memristor memory further includes a reconfigurable interface driver to select an operational mode of the plurality of operational modes of the memristor. The memristor is to operate in the operational mode selected by the reconfigurable interface driver.

1-D memristor networks as ternary storage cells

Abstract: Due to its inherent analog nature, the memristor can store information in a continuous form, being thus well-suited for compact multi-bit memory cell technology. In this context, threshold-type switching devices show great retention and switching speed but still poor controllability. To this end, in this work we use one-dimensional (1-D) networks of anti-serially connected threshold-type memristors as means to to create voltage-controlled ternary memristive switches. We demonstrate that the number of memristors and their polarity define the memristance corresponding to the different stored information. We present a simulation-based study of their performance using a threshold-type switching model of bipolar voltage-controlled memristors, and comment on the applied programming-pulse characteristics and the most important device-level properties.

Memristor-based hardware neural networks modelling review and framework concept

Abstract: This paper is a report of a study in progress that considers development of a framework and environment for modelling hardware memristor-based neural networks. An extensive review of the domain has been performed and partly reported in this work. Fundamental papers on memristors and memristor related technologies have been given attention. Various physical implementations of memristors have mentioned together with several mathematical models of the metal-dioxide memristor group. One of the latter has been given a closer look in the paper by briefly describing model’s mechanisms and some of the important observations. The paper also considers a recently proposed architecture of memristor-based neural networks and suggests enhancing it by replacing the utilized memristor model with a more accurate one. Based on this review, a number of development requirements was derived and formally specified. Ontological and functional models of the domain at hand have been proposed to foster understanding of the corresponding field from different points of view. Ontological model is supposed to shed light onto the object-oriented structure of memristor-based neural network, whereas the functional model exposes the underlying behavior of network’s components which is described in terms of mathematical equations. Finally, the paper shortly speculates about the development platform for the framework and its prospects.