Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing
AbstractA linearized programming method of memristor-based neural weights is proposed. Memristor is known as an ideal element to implement a neural synapse due to its embedded functions of analog memory and analog multiplication. Its resistance variation with a voltage input is generally a nonlinear function of time. Linearization of memristance variation about time is very important for the easiness of memristor programming. In this paper, a method utilizing an anti-serial architecture for linear programming is proposed. The anti-serial architecture is composed of two memristors with opposite polarities. It linearizes the variation of memristance due to complimentary actions of two memristors. For programming a memristor, additional memristor with opposite polarity is employed. The linearization effect of weight programming of an anti-serial architecture is investigated and memristor bridge synapse which is built with two sets of anti-serial memristor architecture is taken as an application example of the proposed method. Simulations are performed with memristors of both linear drift model and nonlinear model. View Full-Text
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Yang, C.; Kim, H. Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing. Sensors 2016, 16, 1320.
Yang C, Kim H. Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing. Sensors. 2016; 16(8):1320.Chicago/Turabian Style
Yang, Changju; Kim, Hyongsuk. 2016. "Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing." Sensors 16, no. 8: 1320.
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