The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Hardware realization of BSB recall function using memristor crossbar arrays
Abstract
The Brain-State-in-a-Box (BSB) model is an auto-associative neural network that has been widely used in optical character recognition and image processing. Traditionally, the BSB model was realized at software level and carried out on high-performance computing clusters. To improve computation efficiency and reduce resources requirement, we propose a hardware realization by utilizing memristor crossbar arrays. In this work, we explore the potential of a memristor crossbar array as an auto-associative memory. More specificly, the recall function of a multi-answer character recognition based on BSB model was realized. The robustness of the proposed BSB circuit was analyzed and evaluated based on massive Monte-Carlo simulations, considering input defects, process variations, and electrical fluctuations. The physical constrains when implementing a neural network with memristor crossbar array have also been discussed. Our results show that the BSB circuit has a high tolerance to random noise. Comparably, the correlations between memristor arrays introduces directional noise and hence dominates the quality of circuits.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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×
“The existence of the memristor was predicted in circuit theory nearly forty year ago [9].”
BackgroundExperimental demonstration of associative memory with memristive neural networks
Pershin, Y. V., & Di Ventra, M. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.05.001 · Cited by 1,041
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
Cited by
Analogue signal and image processing with large memristor crossbars
Li, C., Hu, M., Li, Y., Jiang, H., Ge, N., Montgomery, E., Zhang, J., Song, W., Dávila, N., Graves, C. E., et al. (2017) · Nature Electronics · doi:10.1038/s41928-017-0002-z · Cited by 1,292
Self-citationNeuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
Ankit, A., Hajj, I. E., Chalamalasetti, S. R., Ndu, G., Foltin, M., Williams, R. S., Faraboschi, P., Hwu, W.-M. W., Strachan, J. P., Roy, K., et al. (2019) · Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3297858.3304049 · Cited by 424
Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
Hu, M., Li, H., Chen, Y., Wu, Q., Rose, G. S., & Linderman, R. W. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2296777 · Cited by 414
Self-citationVortex: variation-aware training for memristor X-bar
Liu, B., Li, H., Chen, Y., Li, X., Wu, Q., & Huang, T. (2015) · Proceedings of the 52nd Annual Design Automation Conference · doi:10.1145/2744769.2744930 · Cited by 197
Self-citation
Metadata
- Authors
- Hu, M., Li, H., Wu, Q., & Rose, G. S.
- Venue
- Proceedings of the 49th Annual Design Automation Conference
- Publisher
- ACM
- Published
- 2012-06-03
- Pages
- 498-503
- ISSN
- 0738-100X (ISSN-L)
- OpenAlex
- W1976075132
- Cited by
- 187
- References
- 4
- FWCI
- 15.97
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hardware realization of BSB recall function using memristor crossbar arrays. https://app.cochat.ai/papers/10.1145/2228360.2228448
- The paper
- Hu, M., Li, H., Wu, Q., & Rose, G. S. (2012). Hardware realization of BSB recall function using memristor crossbar arrays. Proceedings of the 49th Annual Design Automation Conference. https://doi.org/10.1145/2228360.2228448
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
