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Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing
- Source :
- Sensors, Vol 16, Iss 8, p 1320 (2016)
- Publication Year :
- 2016
- Publisher :
- MDPI AG, 2016.
-
Abstract
- A 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.
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 16
- Issue :
- 8
- Database :
- Directory of Open Access Journals
- Journal :
- Sensors
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f8f725a58c2412a97028a1456dabeb2
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/s16081320