Abstract
By mimicking the complex biological systems, neuromorphic system is more efficient and less energy-efficient than the traditional Von Neumann architecture. Due to the similarity between memristor and biological synapse, many research efforts have been investigated in utilizing the latest discovered memristor as synapse. This paper improves the original network circuit based on memristor and content addressable memory structure and extends the existing results in the literature. The competition network circuit includes input layer, synapse and output layer. The synapse is made up of two memristors which store information and judge whether input and storage data are same. The output layer consists of subtractor which processes match and mismatch voltage to recognize pattern and the winner-take-all circuit to find out of which storage pattern is the closest to input pattern. The circuit design about read/write framework and working principle are discussed in detail. Finally, the system has been trained and recognizes these \(5 \times 6\) pixel digit images from 0 to 9 successfully.
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Acknowledgments
This work was supported by Huawei Innovation Research Program (HIRP) under Grant YB2015080050, the Science and Technology Support Program of Hubei Province under Grant 2015BHE013, the Program for Science and Technology in Wuhan of China under Grant 2014010101010004, the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant IRT1245 and the National Priority Research Project NPRP 7-1482-1-278 funded by Qatar National Research Fund.
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Zhu, Y., Wang, X., Huang, T., Zeng, Z. (2016). Memristor-Based Neuromorphic System with Content Addressable Memory Structure. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_78
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DOI: https://doi.org/10.1007/978-3-319-40663-3_78
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