From Memory in our Brain to Emerging Resistive Memories in Neuromorphic Systems

DeSalvo, B.; Vianello, E.; Garbin, Daniele; Bichler, Olivier; Perniola, L.

doi:10.1109/imw.2015.7150286

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…In addition to binary operation, analogue memory operations that can be used for artificial neural network hardware have been investigated energetically in recent years. [14][15][16][17] Application of a voltage to a ReRAM device, where the ReRAM material is sandwiched between two electrodes to form a simple capacitor structure, causes the device resistance to switch between the high resistance state (HRS) and the low resistance state (LRS) (and intermediate states in some cases). This switching process can be used for the memory operation.…”

mentioning

confidence: 99%

Microstructural transitions in resistive random access memory composed of molybdenum oxide with copper during switching cycles

et al. 2016

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confidence: 99%

Microstructural transitions in resistive random access memory composed of molybdenum oxide with copper during switching cycles

et al. 2016

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“…As a result, the atomic switch's behaviour can be controlled by the material choice, which is likely to vary on the application. CBRAM has been implemented using GeS₂/Ag [44][45][46][47][48][49][50], HfO₂/GeS₂ [51], Cu/Ti/Al₂O₃ [52], Ag/Ge₀.₃Se₀.₇ [53][54][55], Ag₂S [56][57][58] and Cu/SiO₂ [54]. Similar to atomic switches, the material chosen affects the stability and dependability of the device as well as the switching behaviour of CBRAM devices, Memristors can be used in a wide number of ways.…”

Section: Materials and Devicesmentioning

confidence: 99%

Neuromorphic Computing between Reality and Future Needs

Ahmed¹,

Shereif²

2023

Artificial Intelligence

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Neuromorphic computing is a one of computer engineering methods that to model their elements as the human brain and nervous system. Many sciences as biology, mathematics, electronic engineering, computer science and physics have been integrated to construct artificial neural systems. In this chapter, the basics of Neuromorphic computing together with existing systems having the materials, devices, and circuits. The last part includes algorithms and applications in some fields.

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“…As such, the selection of the appropriate material can govern how the atomic switch will behave and will likely be application-dependent. CBRAM has been implemented using GeS 2 /Ag [299], [457], [1027], [1384], [1439], [2066], [2068], HfO 2 /GeS 2 [2067], Cu/Ti/Al 2 O 3 [2070], Ag/Ge 0.3 Se 0.7 [1408], [2069], [2198], Ag 2 S [2199]- [2201] and Cu/SiO 2 [2069]. Similar to atomic switches, the switching behavior of CBRAM devices is also dependent upon the material selected; the stability and reliability of the device is also dependent upon the material chosen.…”

Section: Materials For Neuromorphic Systemsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

180

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Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

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From Memory in our Brain to Emerging Resistive Memories in Neuromorphic Systems

Cited by 10 publications

References 5 publications

Microstructural transitions in resistive random access memory composed of molybdenum oxide with copper during switching cycles

Microstructural transitions in resistive random access memory composed of molybdenum oxide with copper during switching cycles

Neuromorphic Computing between Reality and Future Needs

A Survey of Neuromorphic Computing and Neural Networks in Hardware

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