Advanced technologies for brain-inspired computing

Clermidy, Fabien; Héliot, Rodolphe; Valentian, Alexandre; Gamrat, Christian; Bichler, Olivier; Duranton, Marc; Blehadj, Bilel; Temam, Olivier

doi:10.1109/aspdac.2014.6742951

Cited by 17 publications

(5 citation statements)

References 27 publications

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“…The applications of resistive crossbar arrays in acceleration of scientific and neuromorphic computing have been studied [13,14]. For example, memristor crossbar was used to implement matrix-vector computation [15].…”

Section: Introductionmentioning

confidence: 99%

A spiking neuromorphic design with resistive crossbar

Liu

Yan

Yang

et al. 2015

Proceedings of the 52nd Annual Design Automation Conference

116

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Neuromorphic systems recently gained increasing attention for their high computation efficiency. Many designs have been proposed and realized with traditional CMOS technology or emerging devices. In this work, we proposed a spiking neuromorphic design built on resistive crossbar structures and implemented with IBM 130nm technology. Our design adopts a rate coding scheme where pre-and post-neuron signals are represented by digitalized pulses. The weighting function of pre-neuron signals is executed on the resistive crossbar in analog format. The computing result is transferred into digitalized output spikes via an integrate-and-fire circuit (IFC) as the postneuron. We calibrated the computation accuracy of the entire system through circuit simulations. The results demonstrated a good match to our analytic modeling. Furthermore, we implemented both feedforward and Hopfield networks by utilizing the proposed neuromorphic design. The system performance and robustness were studied through massive Monte-Carlo simulations based on the application of digital image recognition. Comparing to the previous crossbar-based computing engine that represents data with voltage amplitude, our design can achieve >50% energy savings, while the average probability of failed recognition increase only 1.46% and 5.99% in the feedforward and Hopfield implementations, respectively.

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Section: Introductionmentioning

confidence: 99%

A spiking neuromorphic design with resistive crossbar

Liu

Yan

Yang

et al. 2015

Proceedings of the 52nd Annual Design Automation Conference

116

View full text Add to dashboard Cite

show abstract

“…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%

“…It has also been utilized to stack neuromorphic chips. Through silicon vias (TSVs) are commonly used to physically implement 3D integration approaches for neuromorphic systems [1058], [2066], [2385]- [2387], partially because utilizing TSVs in neuromorphic systems help mitigate some of the issues that arise with using TSVs, such as parasitic capacitance [2388]; however, other technologies have also been utilized in 3D integration, such as microbumps [2389]. 3D integration is commonly used in neuromorphic systems with a variety of other technologies, such as memristors [866], [2390]- [2393], phase change memory [2093], and CMOS-molecular (CMOL) systems [2394].…”

Section: A Communicationmentioning

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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Advanced technologies for brain-inspired computing

Cited by 17 publications

References 27 publications

A spiking neuromorphic design with resistive crossbar

A spiking neuromorphic design with resistive crossbar

Neuromorphic Computing between Reality and Future Needs

A Survey of Neuromorphic Computing and Neural Networks in Hardware

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