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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Miao Hu Hai Li Qing Wu Rose, G.S. Yiran Chen |
| Copyright Year | 2012 |
| Description | Author affiliation: Dept. of Electrical and Computer Engineering, Polytechnic Institute of New York University, Brooklyn, USA 11201 (Miao Hu; Hai Li) || Air Force Research Laboratory, Information Directorate, 525 Brooks Road, Rome, New York, USA 13441 (Qing Wu; Rose, G.S.) || Dept. of Electrical and Computer Engineering, University of Pittsburgh, PA, USA 15261 (Yiran Chen) |
| 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 resource requirement, we propose a hardware realization by utilizing memristor crossbar arrays. Memristors can remember the historical profiles of the excitations and record them as analog variables. The similarity to biological synaptic behavior has encouraged a lot of research on memristor-based neuromorphic hardware system. In this work, we explore the potential of a memristor crossbar array as an auto-associative memory. More specifically, 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 constraints 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 introduce directional noise and hence dominate the quality of the circuit. |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 1260218 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252563 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Memristors Noise Integrated circuit modeling Hardware Neurons Neural networks Robustness process variation neural network BSB model memristor crossbar array |
| Content Type | Text |
| Resource Type | Article |
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