Journal ArticleDOI
Analogue signal and image processing with large memristor crossbars
Can Li,Miao Hu,Miao Hu,Yunning Li,Hao Jiang,Ning Ge,Eric Montgomery,Jiaming Zhang,Wenhao Song,Noraica Davila,Catherine Graves,Zhiyong Li,John Paul Strachan,Peng Lin,Zhongrui Wang,Mark Barnell,Qing Wu,R. Stanley Williams,Jianhua Yang,Qiangfei Xia +19 more
- Vol. 1, Iss: 1, pp 52-59
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TLDR
It is shown that reconfigurable memristor crossbars composed of hafnium oxide memristors on top of metal-oxide-semiconductor transistors are capable of analogue vector-matrix multiplication with array sizes of up to 128 × 64 cells.Abstract:
Memristor crossbars offer reconfigurable non-volatile resistance states and could remove the speed and energy efficiency bottleneck in vector-matrix multiplication, a core computing task in signal and image processing. Using such systems to multiply an analogue-voltage-amplitude-vector by an analogue-conductance-matrix at a reasonably large scale has, however, proved challenging due to difficulties in device engineering and array integration. Here we show that reconfigurable memristor crossbars composed of hafnium oxide memristors on top of metal-oxide-semiconductor transistors are capable of analogue vector-matrix multiplication with array sizes of up to 128 × 64 cells. Our output precision (5–8 bits, depending on the array size) is the result of high device yield (99.8%) and the multilevel, stable states of the memristors, while the linear device current–voltage characteristics and low wire resistance between cells leads to high accuracy. With the large memristor crossbars, we demonstrate signal processing, image compression and convolutional filtering, which are expected to be important applications in the development of the Internet of Things (IoT) and edge computing.read more
Citations
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Journal ArticleDOI
In-memory computing with resistive switching devices
TL;DR: This Review Article examines the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, theirresistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation.
Journal ArticleDOI
Memristive crossbar arrays for brain-inspired computing
Qiangfei Xia,Jianhua Yang +1 more
TL;DR: The challenges in the integration and use in computation of large-scale memristive neural networks are discussed, both as accelerators for deep learning and as building blocks for spiking neural networks.
Journal ArticleDOI
Memory devices and applications for in-memory computing
TL;DR: This Review provides an overview of memory devices and the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing.
Journal ArticleDOI
Fully memristive neural networks for pattern classification with unsupervised learning
Zhongrui Wang,Saumil Joshi,Sergey Savel'ev,Wenhao Song,Rivu Midya,Yunning Li,Mingyi Rao,Peng Yan,Shiva Asapu,Ye Zhuo,Hao Jiang,Peng Lin,Can Li,Jung Ho Yoon,Navnidhi K. Upadhyay,Jiaming Zhang,Miao Hu,John Paul Strachan,Mark Barnell,Qing Wu,Huaqiang Wu,R. Stanley Williams,Qiangfei Xia,Jianhua Yang +23 more
TL;DR: It is shown that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance.
Journal ArticleDOI
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Can Li,Daniel Belkin,Daniel Belkin,Yunning Li,Peng Yan,Peng Yan,Miao Hu,Miao Hu,Ning Ge,Hao Jiang,Eric Montgomery,Peng Lin,Zhongrui Wang,Wenhao Song,John Paul Strachan,Mark Barnell,Qing Wu,R. Stanley Williams,Jianhua Yang,Qiangfei Xia +19 more
TL;DR: This work monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer memristor neural network and achieves competitive classification accuracy on a standard machine learning dataset.
References
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The missing memristor found
TL;DR: It is shown, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage.
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TL;DR: In this article, the memristor is introduced as the fourth basic circuit element and an electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell's equations is presented.