1,553 research outputs found
Experience on material implication computing with an electromechanical memristor emulator
Memristors are being considered as a promising emerging device able to introduce new paradigms in both data storage and computing. In this paper the authors introduce the concept of a quasi-ideal experimental device that emulates the fundamental behavior of a memristor based on an electro- mechanical organization. By using this emulator, results about the experimental implementation of an unconventional material implication-based data-path equivalent to the i-4004 are presented and experimentally demonstrated. The use of the proposed quasi-ideal device allows the evaluation of this new computing paradigm, based on the resistance domain, without incorporating the disturbance of process and cycle to cycle variabilities observed in real nowadays devices that cause a limit in yield and behavior.Peer ReviewedPostprint (published version
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
Fully CMOS Memristor Based Chaotic Circuit
This paper demonstrates the design of a fully CMOS chaotic circuit consisting of only DDCC based memristor and inductance simulator. Our design is composed of these active blocks using CMOS 0.18 µm process technology with symmetric ±1.25 V supply voltages. A new single DDCC+ based topology is used as the inductance simulator. Simulation results verify that the design proposed satisfies both memristor properties and the chaotic behavior of the circuit. Simulations performed illustrate the success of the proposed design for the realization of CMOS based chaotic applications
Toward bio-inspired information processing with networks of nano-scale switching elements
Unconventional computing explores multi-scale platforms connecting
molecular-scale devices into networks for the development of scalable
neuromorphic architectures, often based on new materials and components with
new functionalities. We review some work investigating the functionalities of
locally connected networks of different types of switching elements as
computational substrates. In particular, we discuss reservoir computing with
networks of nonlinear nanoscale components. In usual neuromorphic paradigms,
the network synaptic weights are adjusted as a result of a training/learning
process. In reservoir computing, the non-linear network acts as a dynamical
system mixing and spreading the input signals over a large state space, and
only a readout layer is trained. We illustrate the most important concepts with
a few examples, featuring memristor networks with time-dependent and history
dependent resistances
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