Notice of Retraction: Perceptron Linear Activation Function Design with CMOS-Memristive Circuits

Bexultan Nursultan, Olga Krestinskaya

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


In the last decade, the interest to emulate of the functionality and structure of the human brain to solve the problems related to image processing and pattern recognition, especially using to Artificial Neural Network (ANN), has significantly increased. The capability of ANN to perform at highspeed has been proven to be very useful for various large scale problems. One of the simple ANN models is perceptron. Since the perceptron is the basic form of a neural network, the efficient implementation of an activation functions is required to build the neural network on hardware. As various works introduce the design of sigmoid and tangent activation functions, most of the other activation functions remain an open research problem. This paper describes the design of the perception circuit with the linear activation function based on operational amplifier for memristive crossbar based neural networks. Additionally, the variation of performance with temperature and noise noise analysis of the circuit are presented.
Original languageEnglish (US)
Pages (from-to)231-234
Number of pages4
JournalProceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018
StatePublished - Sep 28 2018
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2023-09-23


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