Probabilistic neuromorphic system using binary phase-change memory (PCM) synapses: Detailed power consumption analysis

Garbin, Daniele; Bichler, Olivier; Querlioz, Damien; Gamrat, Christian; DeSalvo, B.

doi:10.1109/nano.2013.6721057

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…Applications that utilize video have also been common uses of neuromorphic systems. The most common example for video is object recognition within video frames [794], [1073], [1242], [1439], [1562], [1565], [1568], [2082], [2088]- [2090], [2416], [2625]- [2631]. This application does not necessarily require a temporal component, as it can analyze video frames as images.…”

Section: Applicationsmentioning

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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Section: Applicationsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

180

View full text Add to dashboard Cite

show abstract

“…In contrast, to switch the HRS cell to LRS (i.e., set operation), a relatively long and moderate pulse is required to heat the GST between the temperature for crystallization (≈350 °C) and melting (≈610 °C). The PCM can also be operated as an analog memory with continuous conductance states to imitate the synaptic plasticity for neuromorphic applications …”

Section: Inorganic Phase‐change Memoriesmentioning

confidence: 99%

Unconventional Inorganic‐Based Memristive Devices for Advanced Intelligent Systems

Sung

Kim

et al. 2019

Adv Materials Technologies

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The latest progresses in software engineering such as cloud computing, big data analysis, and machine learning have accelerated the emergence of advanced intelligent systems (AIS). However, the current computing system has significant challenges in dealing with unstructured data (e.g., image, voice, physiological signals) because the von‐Neumann bottleneck induces latency and power consumption issues. Neuromorphic computing, which imitates the behaviors of neuron and synapse within the biological neural network, is considered a promising solution beyond von‐Neumann architecture, since its collocated structure of processor and memory enables parallel processing of unstructured data with remarkable efficiency. Memristors are considered as next‐generation nonvolatile memory devices due to fast speed, low power, and excellent scalability. However, a low reliability and leakage current issues remain as obstacle to the commercialization of memristors. Memristive devices have been widely investigated as a strong candidate for artificial synapses since their resistance modulation characteristics under electrical stimulus are analogous to the plasticity of the brain synapse. Although emulation of synaptic behavior by single memristor cells is demonstrated by many researchers, the development of fully functional memristive neural network requires further investigations. This paper introduces the recent advances and developments in the field of inorganic‐based unconventional memristive devices for future AIS applications.

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“…[64] and Wang et al [65]; while aspects related to plasticity of such networks in memristive devices has been studied by Saïghi et al [66]. Garbin et al [67] present phase-change memory (PCM) devices as binary probabilistic synapses in a neuromorphic system for visual pattern recognition. Suri et al [68] analyze the resistance-drift effect in PCMs, which have also been used to develop a large scale neural network by Burr et al [69] and Boybat et al [70].…”

Section: Non-cmos and Hybrid Solutionsmentioning

confidence: 99%

Recent trends in neuromorphic engineering

Soman

Jayadeva

2016

Big Data Anal

Self Cite

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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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Probabilistic neuromorphic system using binary phase-change memory (PCM) synapses: Detailed power consumption analysis

Cited by 14 publications

References 10 publications

A Survey of Neuromorphic Computing and Neural Networks in Hardware

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Unconventional Inorganic‐Based Memristive Devices for Advanced Intelligent Systems

Recent trends in neuromorphic engineering

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