A Memristive Multilayer Cellular Neural Network With Applications to Image Processing
Top Cited Papers
- 13 May 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 28 (8), 1889-1901
- https://doi.org/10.1109/tnnls.2016.2552640
Abstract
The memristor has been extensively studied in electrical engineering and biological sciences as a means to compactly implement the synaptic function in neural networks. The cellular neural network (CNN) is one of the most implementable artificial neural network models and capable of massively parallel analog processing. In this paper, a novel memristive multilayer CNN (Mm-CNN) model is presented along with its performance analysis and applications. In this new CNN design, the memristor crossbar circuit acts as the synapse, which realizes one signed synaptic weight with a pair of memristors and performs the synaptic weighting compactly and linearly. Moreover, the complex weighted summation is executed in an efficient way with a proper design of Mm-CNN cell circuits. The proposed Mm-CNN has several merits, such as compactness, nonvolatility, versatility, and programmability of synaptic weights. Its performance in several image processing applications is illustrated through simulations.Keywords
Funding Information
- Fundamental Research Funds for the Central Universities (XDJK2014A009, XDJK2016C149)
- University Excellent Talents Supporting Foundations, Chongqing, China (2011-65)
- Program for New Century Excellent Talents in University ([2013]47)
- Doctoral Foundation of Southwest University (SWU116005)
- National Natural Science Foundation of China (60972155, 61101233, 61372139, 61571372)
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