Literature DB >> 22260949

High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm.

Fabien Alibart1, Ligang Gao, Brian D Hoskins, Dmitri B Strukov.   

Abstract

Using memristive properties common for titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to seven-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is nonvolatile and the results are likely to be sustained for nanoscale memristive devices because of the inherent filamentary nature of the resistive switching. The proposed functionality of memristive devices is especially attractive for analog computing with low precision data. As one representative example we demonstrate hybrid circuitry consisting of an integrated circuit summing amplifier and two memristive devices to perform the analog multiply-and-add (dot-product) computation, which is a typical bottleneck operation in information processing.

Entities:  

Year:  2012        PMID: 22260949     DOI: 10.1088/0957-4484/23/7/075201

Source DB:  PubMed          Journal:  Nanotechnology        ISSN: 0957-4484            Impact factor:   3.874


  19 in total

1.  Memristive devices for computing.

Authors:  J Joshua Yang; Dmitri B Strukov; Duncan R Stewart
Journal:  Nat Nanotechnol       Date:  2013-01       Impact factor: 39.213

Review 2.  Applications and Techniques for Fast Machine Learning in Science.

Authors:  Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier Duarte; Philip Harris; Scott Hauck; Mia Liu; Mark S Neubauer; Jennifer Ngadiuba; Seda Ogrenci-Memik; Maurizio Pierini; Thea Aarrestad; Steffen Bähr; Jürgen Becker; Anne-Sophie Berthold; Richard J Bonventre; Tomás E Müller Bravo; Markus Diefenthaler; Zhen Dong; Nick Fritzsche; Amir Gholami; Ekaterina Govorkova; Dongning Guo; Kyle J Hazelwood; Christian Herwig; Babar Khan; Sehoon Kim; Thomas Klijnsma; Yaling Liu; Kin Ho Lo; Tri Nguyen; Gianantonio Pezzullo; Seyedramin Rasoulinezhad; Ryan A Rivera; Kate Scholberg; Justin Selig; Sougata Sen; Dmitri Strukov; William Tang; Savannah Thais; Kai Lukas Unger; Ricardo Vilalta; Belina von Krosigk; Shen Wang; Thomas K Warburton
Journal:  Front Big Data       Date:  2022-04-12

3.  Complex Learning in Bio-plausible Memristive Networks.

Authors:  Lei Deng; Guoqi Li; Ning Deng; Dong Wang; Ziyang Zhang; Wei He; Huanglong Li; Jing Pei; Luping Shi
Journal:  Sci Rep       Date:  2015-06-19       Impact factor: 4.379

4.  Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors.

Authors:  M Prezioso; F Merrikh Bayat; B Hoskins; K Likharev; D Strukov
Journal:  Sci Rep       Date:  2016-02-19       Impact factor: 4.379

5.  Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits.

Authors:  M Prezioso; M R Mahmoodi; F Merrikh Bayat; H Nili; H Kim; A Vincent; D B Strukov
Journal:  Nat Commun       Date:  2018-12-14       Impact factor: 14.919

6.  Ultrafast synaptic events in a chalcogenide memristor.

Authors:  Yi Li; Yingpeng Zhong; Lei Xu; Jinjian Zhang; Xiaohua Xu; Huajun Sun; Xiangshui Miao
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

7.  Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

Authors:  Evangelos Stromatias; Daniel Neil; Michael Pfeiffer; Francesco Galluppi; Steve B Furber; Shih-Chii Liu
Journal:  Front Neurosci       Date:  2015-07-09       Impact factor: 4.677

8.  Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits.

Authors:  Xinjie Guo; Farnood Merrikh-Bayat; Ligang Gao; Brian D Hoskins; Fabien Alibart; Bernabe Linares-Barranco; Luke Theogarajan; Christof Teuscher; Dmitri B Strukov
Journal:  Front Neurosci       Date:  2015-12-24       Impact factor: 4.677

9.  Generalized reconfigurable memristive dynamical system (MDS) for neuromorphic applications.

Authors:  Mohammad Bavandpour; Hamid Soleimani; Bernabé Linares-Barranco; Derek Abbott; Leon O Chua
Journal:  Front Neurosci       Date:  2015-11-03       Impact factor: 4.677

10.  Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits.

Authors:  F Merrikh Bayat; M Prezioso; B Chakrabarti; H Nili; I Kataeva; D Strukov
Journal:  Nat Commun       Date:  2018-06-13       Impact factor: 14.919

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.