The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Memristor Bridge Synapse-Based Neural Network and Its Learning
Abstract
Analog hardware architecture of a memristor bridge synapse-based multilayer neural network and its learning scheme is proposed. The use of memristor bridge synapse in the proposed architecture solves one of the major problems, regarding nonvolatile weight storage in analog neural network implementations. To compensate for the spatial nonuniformity and nonideal response of the memristor bridge synapse, a modified chip-in-the-loop learning scheme suitable for the proposed neural network architecture is also proposed. In the proposed method, the initial learning is conducted in software, and the behavior of the software-trained network is learned by the hardware network by learning each of the single-layered neurons of the network independently. The forward calculation of the single-layered neuron learning is implemented on circuit hardware, and followed by a weight updating phase assisted by a host computer. Unlike conventional chip-in-the-loop learning, the need for the readout of synaptic weights for calculating weight updates in each epoch is eliminated by virtue of the memristor bridge synapse and the proposed learning scheme. The hardware architecture along with the successful implementation of proposed learning on a three-bit parity network, and on a car detection network is also presented.
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Cites
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Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 3×
“L. O. Chua is with the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720-1770 USA.”
BackgroundSelf-citationNanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
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Multilayer neural-net robot controller with guaranteed tracking performance
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The elusive memristor: properties of basic electrical circuits
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Control of a nonholonomic mobile robot using neural networks
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Cited by
If it's pinched it's a memristor
Chua, L. (2014) · Semiconductor Science and Technology · doi:10.1088/0268-1242/29/10/104001 · Cited by 685
Self-citationNeuromemristive Circuits for Edge Computing: A Review
Krestinskaya, O., James, A. P., & Chua, L. O. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2899262 · Cited by 319
Self-citationNeuromorphic Hardware System for Visual Pattern Recognition With Memristor Array and CMOS Neuron
Chu, M., Kim, B., Park, S., Hwang, H., Jeon, M., Lee, B. H., & Lee, B.-G. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2356439 · Cited by 296
Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training
Soudry, D., Di Castro, D., Gal, A., Kolodny, A., & Kvatinsky, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2383395 · Cited by 271
Adaptive Synchronization of Memristor-Based Neural Networks with Time-Varying Delays
Wang, L., Shen, Y., Yin, Q., & Zhang, G. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2361776 · Cited by 200
Metadata
- Authors
- Adhikari, S. P., Yang, C., Kim, H., & Chua, L. O.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2012-09
- Volume
- 23
- Issue
- 9
- Pages
- 1426-1435
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2080625874
- Cited by
- 386
- References
- 25
- FWCI
- 14.04
- Type
- journal article
- Language
- en
- Open access
- closed
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