The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
Abstract
By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated in utilizing the latest discovered memristors in neuromorphic systems due to the similarity of memristors to biological synapses. In this paper, we explore the potential of a memristor crossbar array that functions as an autoassociative memory and apply it to brain-state-in-a-box (BSB) neural networks. Especially, the recall and training functions of a multianswer character recognition process based on the BSB model are studied. The robustness of the BSB circuit is analyzed and evaluated based on extensive Monte Carlo simulations, considering input defects, process variations, and electrical fluctuations. The results show that the hardware-based training scheme proposed in the paper can alleviate and even cancel out the majority of the noise issue.
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Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 1×
“Thus, a more efficient hardware-level solution is necessary.”
BackgroundNanoscale 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
Resistance switching memories are memristors
Chua, L. (2011) · Applied Physics A · doi:10.1007/s00339-011-6264-9 · Cited by 1,434
Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors
Di Ventra, M., Pershin, Y. V., & Chua, L. O. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2021077 · Cited by 1,212
Kim, K.-H., Gaba, S., Wheeler, D., Cruz-Albrecht, J. M., Hussain, T., Srinivasa, N., & Lu, W. (2012) · Nano Letters · doi:10.1021/nl203687n · Cited by 924
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Memristor−CMOS Hybrid Integrated Circuits for Reconfigurable Logic
Xia, Q., Robinett, W., Cumbie, M. W., Banerjee, N., Cardinali, T. J., Yang, J. J., Wu, W., Li, X., Tong, W. M., Strukov, D. B., et al. (2009) · Nano Letters · doi:10.1021/nl901874j · Cited by 671
Practical Approach to Programmable Analog Circuits With Memristors
Pershin, Y. V., & Di Ventra, M. (2010) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2009.2038539 · Cited by 639
Neural Synaptic Weighting With a Pulse-Based Memristor Circuit
Kim, H., Sah, M. P., Yang, C., Roska, T., & Chua, L. O. (2012) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2011.2161360 · Cited by 348
Cited by
Ferroelectric Analog Synaptic Transistors
Kim, M.-K., & Lee, J.-S. (2019) · Nano Letters · doi:10.1021/acs.nanolett.9b00180 · Cited by 594
Hardware implementation of memristor-based artificial neural networks
Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45670-9 · Cited by 564
Neuromemristive 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
XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks
Sun, X., Yin, S., Peng, X., Liu, R., Seo, J.-S., & Yu, S. (2018) · 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) · doi:10.23919/date.2018.8342235 · Cited by 246
Resistive memory device requirements for a neural algorithm accelerator
Agarwal, S., Plimpton, S. J., Hughart, D. R., Hsia, A. H., Richter, I., Cox, J. A., James, C. D., & Marinella, M. J. (2016) · 2016 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727298 · Cited by 213
Metadata
- Authors
- Hu, M., Li, H., Chen, Y., Wu, Q., Rose, G. S., & Linderman, R. W.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2014-10
- Volume
- 25
- Issue
- 10
- Pages
- 1864-1878
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2021383442
- Cited by
- 414
- References
- 28
- FWCI
- 15.56
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hu, M., Li, H., Chen, Y., Wu, Q., Rose, G. S., & Linderman, R. W. (2014). Memristor Crossbar-Based Neuromorphic Computing System: A Case Study. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2013.2296777
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