Search

Your search keyword '"Chen, Wanlong"' showing total 11 results
Start Over Author "Chen, Wanlong" Language english
11 results on '"Chen, Wanlong"'

1. Memristor content addressable memory : theory, design and application

Author

Chen, Wanlong and Wang, Frank

Subjects
621.3815
Abstract

The memristor has been proposed as the fourth circuit element. Among the emerging nano-technologies, the memristor has become a very promising candidate for building storage structures because of its shorter switching time, higher capacity and lower power consumption. In this thesis, I will first introduce a new memristor model with controllable window functions, which is more authentic and flexible than those existing memristor models. Then I will present my novel design of a Memristor Content Addressable Memory (Memristor-CAM) structure that is based on my own design of Memristor-CAM cells. The major contribution of this work is the fuzzy look-up functionality, which is achieved by summing up the current of the matched cell lines in the Memristor-CAM. In addition, this fuzzy look-up functionality of the new Memristor-CAM design could be further extended in order to fit into a lot of practical applications. With the benefits of memristors, this Memristor-CAM storage structure could reduce the power consumption, increase the capacity and improve the performance of computer memory. My new design is tested in a common experimental design that includes computer simulations and circuit emulations. The results of my experiments support the validity of my contributions and allow further analysis and insights on the behaviours of memristors when different settings are applied.

Published
2017

6. Delayed Switching Applied to Memristor Content Addressable Memory Cell

Author

Chen, Wanlong, Yang, Xiao, and Wang, Frank Z.

Subjects
QA75, Hardware_MEMORYSTRUCTURES
Abstract

Memristor is short for memory resistor, which provides a functional relation between flux and charge. Professor Leon Chua named and formulated it in his paper\ud "Memristor—The Missing Circuit Element" in 1971. The memristor has a special effect, ‘the delayed switching effect’, which is the memristor switching takes place with a time delay. Content addressable memory (CAM) is a type of associative memories that is adopted in high speed searching applications. The new Memristor-CAM cell has been proposed, which included two memristors; one is used as an important part of the comparator that is instead of the traditional logic gate; and another one is for storing the data as a storage element. In this paper, we report that applying the memristor delayed switching effect to the novel design of the Memristor Content Addressable Memory (M-CAM) cell. The delayed switching effect is used to control the changing time of the memristor’s state, which can enhance the performance, decrease the searching time and save energy.

Published
2013

10. A supervised spiking time dependant plasticity network based on memristors.

Author

Yang, Xiao, Chen, Wanlong, and Wang, Frank Z.

Abstract

Synaptic plasticity has been widely assumed to be the mechanism behind memory and learning, in which, synapse has a critical role. As a newer biologic update rule to hebbian learning, spiking-time dependent plasticity (STDP) concerns on the temporal order of presynaptic spike and postsynaptic spike which will change the strength of, the connection site of neurons, synapse. In this paper a different way is shown to utilise the novel element memristors to implement a supervised STDP. Because the resistance of memristor depends on its past states, researchers are particularly interested in using such functionality to mimic synaptic connection. Furthermore, benefit from the nano size of memristors and its crossbar structure, large scale neural networks could be implemented. In this supervised STDP, each spike arrival will be assumed to leave a trace which decays exponentially and spikes interact under all-to-all interaction. Depending on the temporal order, memristor synapse will weaken or strengthen the connection of presynaptic neuron and postsynaptic neuron. The temporal all-to-all interaction is implemented during the simulation with training samples. We show that, by combining the memristors, a supervised STDP neural network can be built and learn from the temporal order of presynaptic spike and postsynaptic spike of the training samples. [ABSTRACT FROM PUBLISHER]

Published
2013
Full Text
View/download PDF

11. School of Computing Postgraduate Conference 2013

Author

Knox, Daniel, Cooke, Jennifer, Berry, Michael, Salama, Khalid, Akanmu, Gbola, Chen, Wanlong, Greenhow, Keith, and Jungjit, Suwimol

Subjects
ComputingMilieux_GENERAL, education, ComputingMilieux_COMPUTERSANDEDUCATION, QA76
Abstract

The published technical report for the School of Computing postgraduate mini conference 2013.

Published
2013

Catalog

Books, media, physical & digital resources
See catalog results