Investigation of the potentialities of Vertical Resistive RAM (VRRAM) for neuromorphic applications

Piccolboni, G.; Molas, G.; Portal, J.M.; Coquand, R.; Bocquet, Marc; Garbin, Daniele; Vianello, Elisa; Carabasse, C.; Delaye, V.; Pellissier, C.; Magis, T.; Cagli, C.; Gély, M.; Cueto, O.; Deleruyelle, Damien; Ghibaudo, G.; Salvo, B. De; Perniola, L.

doi:10.1109/iedm.2015.7409717

Cited by 28 publications

(17 citation statements)

References 3 publications

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“…Bill et al reported the computational implications of synaptic plasticity through stochastic filament formation in multiple binary RRAM cells tied together in parallel to form a single compound stochastic synapse [156]. A HfO 2 -based vertical RRAM (VRRAM) technology was recently reported by Piccolboni et al [157]. Each synapse is composed of stack of VRRAM devices (each exhibiting only two distinct resistance states) with one common select transistor, exhibiting analog conductance behavior through the parallel configuration of N RRAM cells.…”

Section: Filamentary Rram As a Synapsementioning

confidence: 99%

Neuromorphic computing using non-volatile memory

Burr

Shelby

Sebastian

et al. 2016

Advances in Physics: X

827

701

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Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and 'Memcomputing'. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix-vector multiplication needed for algorithms such as backpropagation in an analog yet massively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices-including phase change memory, conductive-bridging RAM, filamentary and nonfilamentary RRAM, and other NVMs-have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.

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Section: Filamentary Rram As a Synapsementioning

confidence: 99%

Neuromorphic computing using non-volatile memory

Burr

Shelby

Sebastian

et al. 2016

Advances in Physics: X

827

701

View full text Add to dashboard Cite

show abstract

“…Another approach is to combine the abrupt SET operation and binary synaptic devices to implement a stochastic learning rule [64]. Piccolboni et al reported a H f O 2 -based vertical RRAM (VRRAM) technology [65], where each synapse is composed of a stack of RRAMs with one common transistor.…”

Section: Fundamental Components 411 Synapsesmentioning

confidence: 99%

Neuromorphic Computing Systems: From CMOS To Emerging Nonvolatile Memory

Yang

Qiao

Chen

2019

IPSJ Transactions on System LSI Design Methodology

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The end of Moore's Law and von Neumann bottleneck motivate researchers to seek alternative architectures that can fulfill the increasing demand for computation resources which cannot be easily achieved by traditional computing paradigm. As one important practice, neuromorphic computing systems (NCS) are proposed to mimic biological behaviors of neurons and synapses, and accelerate computation of neural networks. Traditional CMOS-based implementation of NCS, however, are subject to large hardware cost required to precisely replicate the biological properties. In very recent decade, emerging nonvolatile memory (eNVM) was introduced to NCS design due to its high computing efficiency and integration density. Similar to the circuits built on other nanoscale devices, eNVM-based NCS also suffers from many reliability issues. In this paper, we give a short survey about CMOS-and eNVM-based NCS, including their basic implementations and training and inference schemes in various applications. We also discuss the design challenges of these NCS and introduce some techniques that can improve the reliability, precision, scalability, and security of the NCS. At the end, we provide our insights on the design trend and future challenges of the NCS.

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“…Synaptic devices for visual systems using Resistive RAMs have been presented by Kang et al [90], while multistate registers have been developed by Patel et al [91]. Vertical RRAMs have been explored for cochlea and convolutional neural nets by Piccolboni [92], while OxRAM synapses for CNNs have been presented by Garbin et al [93]. ReRAM devices for neuromorphic computing have been explored by Jang et al [94], while an artificial synapse using a memristive switch has been modelled by Wang et al [95].…”

Section: Analog Implementationsmentioning

confidence: 99%

Recent trends in neuromorphic engineering

Soman

Jayadeva

2016

Big Data Anal

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Neuromorphic Engineering has emerged as an exciting research area, primarily owing to the paradigm shift from conventional computing architectures to data-driven, cognitive computing. There is a diversity of work in the literature pertaining to neuromorphic systems, devices and circuits. This review looks at recent trends in neuromorphic engineering and its sub-domains, with an attempt to identify key research directions that would assume significance in the future. We hope that this review would serve as a handy reference to both beginners and experts, and provide a glimpse into the broad spectrum of applications of neuromorphic hardware and algorithms. Our survey indicates that neuromorphic engineering holds a promising future, particularly with growing data volumes, and the imminent need for intelligent, versatile computing.

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Investigation of the potentialities of Vertical Resistive RAM (VRRAM) for neuromorphic applications

Cited by 28 publications

References 3 publications

Neuromorphic computing using non-volatile memory

Neuromorphic computing using non-volatile memory

Neuromorphic Computing Systems: From CMOS To Emerging Nonvolatile Memory

Recent trends in neuromorphic engineering

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