106 research outputs found
Reliable Modeling of Ideal Generic Memristors via State-Space Transformation
The paper refers to problems of modeling and computer simulation of generic memristors caused by the so-called window functions, namely the stick effect, nonconvergence, and finding fundamentally incorrect solutions. A profoundly different modeling approach is proposed, which is mathematically equivalent to window-based modeling. However, due to its numerical stability, it definitely smoothes the above problems away
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
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
Reliable SPICE Simulations of Memristors, Memcapacitors and Meminductors
Memory circuit elements, namely memristive, memcapacitive and meminductive systems, are gaining considerable attention due to their ubiquity and use in diverse areas of science and technology. Their modeling within the most widely used environment, SPICE, is thus critical to make substantial progress in the design and analysis of complex circuits. Here, we present a collection of models of different memory circuit elements and provide a methodology for their accurate and reliable modeling in the SPICE environment. We also provide codes of these models written in the most popular SPICE versions (PSpice, LTspice, HSPICE) for the benefit of the reader. We expect this to be of great value to the growing community of scientists interested in the wide range of applications of memory circuit elements
Building memristor applications: from device model to circuit design
Since the memristor was first built in 2008 at HP Labs, no end of devices and models have been presented. Also, new applications appear frequently. However, the integration of the device at the circuit level is not straightforward, because available models are still immature and/or suppose high computational loads, making their simulation long and cumbersome. This study assists circuit/systems designers in the integration of memristors in their applications, while aiding model developers in the validation of their proposals. We introduce the use of a memristor application framework to support the work of both the model developer and the circuit designer. First, the framework includes a library with the best-known memristor models, being easily extensible with upcoming models. Systematic modifications have been applied to these models to provide better convergence and significant simulations speedups. Second, a quick device simulator allows the study of the response of the models under different scenarios, helping the designer with the stimuli and operation time selection. Third, fine tuning of the device including parameters variations and threshold determination is also supported. Finally, SPICE/Spectre subcircuit generation is provided to ease the integration of the devices in application circuits. The framework provides the designer with total control overconvergence, computational load, and the evolution of system variables, overcoming usual problems in the integration of memristive devices
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