Collaborative Research: SMURFS: Statistical Modeling, SimUlation and Robust Design Techniques For MemriStors

The fourth fundamental passive circuit element − memristor, has demonstrated great potentials in massive data storage, neuromorphic computing, signal processing, biomedical lab-on-a-chip, sensing etc. The objective of this research is to investigate the design implications of process variations and environmental fluctuations to memristor-based VLSI systems, to exploit a fast statistical simulation technique, and to explore new circuit techniques to improve the memristive system reliability and robustness.

Intellectual Merit: This research includes three integrated components: (1) The statistical device models for representative memristor technologies, i.e., the TiO2 thin-film and spintronic memristors, will be developed to facilitate the process variation aware design space explorations; (2) The fast Monte-Carlo simulation platform will be developed for memristor-based VLSI system designs and simulations; (3) By leveraging the statistical memristor models and simulation platform, robust circuit design techniques that can minimize the fluctuations of the electrical properties caused by process variations will be investigated.

Broader Impacts: This research provides a comprehensive design package for efficiently integrating memristor into existing VLSI systems to offer better performance and power consumption. The device engineers and the circuit designers are well bridged and educated by the research innovations. The developed techniques can be directly transferred to industry applications under the close collaborations with leading industry partners, and directly impact the future memristor-based VLSI systems.

 

Senior Personnel: Prof. Hai (Helen) Li (PI) and Prof. Yiran Chen (PI)
Sponsors: National Science Foundation (Prof. Li’s Award)

National Science Foundation (Prof. Chen’s Award)

Team Members:  Ling Chen, Miao Hu, Beiye Liu, Chenchen Liu, and Lu Zhang
Collaborators: Prof. Tingwen Huang, Texas A&M University at Qatar
Prof. Chuandong Li, Southwest University
Dr. Robinson E. Pino, U.S. Department of Energy
Dr. Qing Wu, U.S. Air Force Research Lab
Dr. Jianhua Yang, HP Labs
 Starting Date:  05/01/2012

Related Publications:

[In Press]

[2014]

[Springer] 

Y. Chen, H. Li and Z. Sun, Spintronic Memristor as Interface between DNA and Solid State Devices, (in Memristors and Memristive Systems, Editor: Ronald Tetzlaff), Springer, Jan. 1, 2014. ISBN: 978-1-4614-9067-8.download-icon

[TNNLS]
M. Hu, H. Li, Y. Chen, Q. Wu, G. Rose, and R. Linderman, “Memristor Crossbar Based Neuromorphic Computing System: A Case Study,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 25, no 10, pp. 1864-1878, Oct. 2014. DOI: 10.1109/TNNLS.2013.2296777.download-icon

[IJCNN]
L. Chen, C. Li, T. Huang, X. He, H. Li and Y. Chen, “STDP Learning Rule Based on Memristor with STDP Property,” International Joint Conference on Neural Networks (IJCNN), Jul. 2014, pp. 1-6. DOI: 10.1109/IJCNN.2014.6889506.download-icon

[ASPDAC]
M. Hu, Y. Wang, Q. Qiu, Y. Wang, Y. Chen, and H. Li, “The Stochastic Modeling of TiO2 Memristor and Its Usage in Neuromorphic System Design,” Asia and South Pacific Design Automation Conference (ASPDAC), Jan. 2014, pp. 831-836. DOI: 10.1109/ASPDAC.2014.6742993.download-icon

[DATE-WIP]
B. Li, Y. Wang, Y. Chen, H. Li, and H. Yang, “ICE: Inline Calibration for Memristor Crossbar-based Computing Engine,” Design, Automation & Test in Europe (DATE), Mar. 2014, pp. 1-4. DOI: 10.7873/ DATE.2014.197.download-icon

 

[2013]

[PLA]
L. Chen, C. Li, T. Huang, Y. Chen, S. Wen, and J. Qi, “A Synapse Memristor Model with Forgetting Effect,” Physics Letters A (PLA), vol. 377, no. 45-48, Dec. 2013, pp. 3260-3265. DOI: 10.1016/j.physleta.2013.10. 024.download-icon

[NPL]
B. Liu, Y. Chen, B. Wysocki, and T. Huang, “Reconfigurable Neuromorphic Computing System with Memristor-Based Synapse Design,” Neural Processing Letters (NPL), Aug. 2013, pp. 1-9. DOI: 10.1007/ s11063-013-9315-8.download-icon

[APL]
L. Zhang, Z. Chen, J. J. Yang, B. Wysocki, N. McDonald and Y. Chen, “A Compact Modeling of TiO2-TiO2-x Memristor,”  Applied Physics Letters (APL), vol. 102, no. 15, 153503 (2013). DOI: 10.1063/ 1.4802206.download-icon

[IJCNN]
F. Ji, H. Li, B. Wysocki, C. Thiem, and N. McDonald “Memristor-based Synapse Design and a Case Study in Reconfigurable Systems,” International Joint Conference on Neural Networks (IJCNN), Aug. 2013, pp. 1-6. DOI: 10.1109/IJCNN.2013.6706776.download-icon

[DAC]
B. Liu, M. Hu, H. Li, Z.-H. Mao, Y. Chen, T. Huang, and W. Zhang, “Digital-Assisted Noise Eliminating Training for Memristor Crossbar-based Analog Neuromorphic Computing Engine,” Design Automation Conference (DAC), Jun. 2013, Article 7. DOI: 10.1145/2463209.2488741.download-icon

[CISDA]
M. Hu, H. Li, Y. Chen, Q. Wu, G. S. Rose, “BSB Training Scheme Implementation on Memristor-Based Circuit,” IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), Apr. 2013, pp. 80-87. DOI: 10.1109/CISDA.2013.6595431.download-icon

[ISDRS-wkp]
M. Hu and H. Li, “The Stochastic Characteristics of Memristor Devices and Case Studies in Neuromorphic Hardware Design,” International Semiconductor Device Research Symposium (ISDRS), Dec. 2013.download-icon

[NeuComp-wkp]
M. Hu, H. Li, G. Rose, Q. Wu and Y. Chen, “Training Scheme Analysis for Memristor-Based Neuromorphic Design,” International Workshop on Neuromorphic and Brain-Based Computing Systems (NeuComp), Mar. 2013.download-icon

[SIGDA-news]
Y. Chen, “What is Nueromorphic Computing,” ACM’s Special Interest Group on Design Automation (SIGDA) E-newsletter, Jan. 1, 2013.

 

[2012]

[Springer]
H. Li and R. E. Pino, “Statistical Memristor Model and Its Applications in Neuromorphic Computing,” (in Advances in Neuromorphic Memristor Science and Applications, Editor: R. Kozma, R. E. Pino, and G. Pazienza), Springer, Jun. 28, 2012. ISBN: 978-94-007-4490-5.download-icon

[ICONIP]
B. Liu, Y. Chen, B. Wysocki, and T. Huang, “The Circuit Realization of a Neuromorphic Computing System with Memristor-based Synapse Design,” International Conference on Neural Information Processing (ICONIP), Nov. 2012, pp. 357-365. DOI: 10.1007/978-3-642-34475-6_43. (Published on Neural Information Processing Lecture Notes in Computer Science Volume 7663, 2012)download-icon

[ICSICT]
H. Li, Z. Sun, X. Bi, and B. Wysocki, “Spintronic Devices: from Memory to Memristor,” International Conference on Solid-State and Integrated Circuit Technology (ICSICT), Oct. 2012, pp. 1-4. DOI: 10.1109/ICSICT.2012.6467793. (Invited)download-icon

[SOCC]
H. Li, “Memristor in Neuromorphic Computing,” IEEE International SoC Conference (SoCC), Sep. 2012, pp. 294. DOI: 10.1109/SOCC.2012.6398367. (Invited)download-icon

[IJCNN]
M. Hu, H. Li, Q. Wu, G. S. Rose, and Y. Chen, “Memristor Crossbar Based Hardware Realization of BSB Recall Function,” International Joint Conference on Neural Networks (IJCNN), Jun. 2012, pp. 1-7. DOI: 10.1109/IJCNN.2012.6252563.download-icon

[DAC]
R. Pino, H. Li, Y. Chen, M. Hu and B. Liu, “Statistical Memristor Modeling and Case Study in Neuromorphic Computing,” Design Automation Conference (DAC), Jun. 2012, pp. 585-590. DOI: 10.1145/2228360.2228466. (Invited)download-icon