Cellular neural network formed by simplified processing elements composed of thin-film transistors

Kimura, Mutsumi; Morita, Ryohei; Sugisaki, Sumio; Matsuda, Tokiyoshi; Kameda, Tomoya; Nakashima, Yasuhiko

doi:10.1016/j.neucom.2016.10.085

Cited by 26 publications

(9 citation statements)

References 14 publications

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“…Therefore, researches to realize neuromorphic computing built on actual hardware are actively being carried out [15,16]. It is possible to achieve high performance, large scale integration, low power consumption, etc., using the neuromorphic computing because the biological brain can achieve them [17][18][19]. In such a system, it is necessary to simplify the configuration of the neuron and the synapse and make it inexpensive.…”

Section: Non-neumann-type Computingmentioning

confidence: 99%

Influence of characteristic variation of oxide semiconductor and comparison of the activation function in neuromorphic hardware

Ikeda

Yamane

Takishita

et al. 2020

NOLTA

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As the amount of data that people handle increases, the conventional Neumann-type computer architecture is reaching its limits. Therefore, research on hardware implementation of machine learning systems is being actively conducted. In this paper, we have implemented and evaluated neuromorphic hardware that realizes human brain neurons and synapses using oxide semiconductor of amorphous In-Ga-Zn-O (a-IGZO) and a cellular neural network. It was confirmed how variations of initial resistance and deterioration rate of the oxide semiconductor affect operation accuracy of the neuromorphic hardware. Furthermore, we clarified that an activation function suitable for the hardware implementation is a ReLU function.

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Section: Non-neumann-type Computingmentioning

confidence: 99%

Influence of characteristic variation of oxide semiconductor and comparison of the activation function in neuromorphic hardware

Ikeda

Yamane

Takishita

et al. 2020

NOLTA

Self Cite

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“…China. 2 School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, P.R. China.…”

Section: Competing Interestsmentioning

confidence: 99%

Dynamics of FCNNs with proportional delays and leakage delays

Chen

Guo

et al. 2018

Adv Differ Equ

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In this article, a kind of fuzzy cellular neural networks (FCNNs) with proportional delays and leakage delays are involved. Utilizing the differential inequality strategies, a sequence of sufficient criteria ensuring the global exponential convergence of involved model are presented. Computer simulations are performed to verify the analytic findings. The analytic findings of this article are innovative and complete several existing works.

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“…Recently, extensive research has been conducted into the development of power-efficient HW-NNs via the development of artificial synapses with high scalability, high power efficiency, and high capabilities to implement learning rules. Such emerging synapse devices exploit various structures and materials including (i) diode-type devices such as resistive random access memory (RRAM), − phase change memory (PCM), ,− and conductive bridging RAM (CBRAM) − and (ii) transistor-type devices such as field-effect transistors (FET), ,− ferroelectric FET (FeFET), − electrochemical transistors, , thin-film devices, − and optoelectronic transistors. − …”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Artificial Synapses Based on Two-Dimensional van der Waals Materials for Brain-Inspired Computing

Seo

Lee

et al. 2020

ACS Appl. Electron. Mater.

127

112

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On the basis of recent research, brain-inspired parallel computing is considered as one of the most promising technologies for efficiently handling large amounts of informational data. In general, this type of parallel computing is called neuromorphic computing; it operates on the basis of hardware-neural-network (HW-NN) platforms consisting of numerous artificial synapses and neurons. Extensive research has been conducted to implement artificial synapses with characteristics required to ensure high-level performance of HW-NNs in terms of device density, energy efficiency, and learnings accuracy. Recently, artificial synapsesspecifically, diode- and transistor-type synapsesbased on various two-dimensional (2D) van der Waals (vdW) materials have been developed. Unique properties of such 2D vdW materials allow for notable improvements in synaptic performances in terms of learning capability, scalability, and power efficiency, thereby highlighting the feasibility of the 2D vdW synapses in improving the performance of HW-NNs. In this review, we introduce the desirable characteristics of artificial synapses required to ensure high-level performance of neural networks. Recent progress in research on artificial synapses, fabricated particularly using 2D vdW materials and heterostructures, is comprehensively discussed with respect to the weight-update mechanism, synaptic characteristics, power efficiency, and scalability.

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Cellular neural network formed by simplified processing elements composed of thin-film transistors

Cited by 26 publications

References 14 publications

Influence of characteristic variation of oxide semiconductor and comparison of the activation function in neuromorphic hardware

Influence of characteristic variation of oxide semiconductor and comparison of the activation function in neuromorphic hardware

Dynamics of FCNNs with proportional delays and leakage delays

Recent Progress in Artificial Synapses Based on Two-Dimensional van der Waals Materials for Brain-Inspired Computing

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