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Chris Yakopcic

Research Scientist

Adjunct

School of Engineering: Department of Electrical and Computer Engineering

Contact

Email: Chris Yakopcic
Phone: 937-229-3611
Kettering Laboratories Room 261 K
Website: Visit Site

Selected Publications

  • Yakopcic, C., Taha, T. M., Mountain, D. J., Salter, T., Marinella, M. J., & McLean, V. (2020, May). Memristor model optimization based on parameter extraction from device characterization data. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(5), 1804-1095.
  • Yakopcic, C., Rahman, N., Atahary, T., Taha, T. M., & Douglass, S. (2020, March). Solving constraint satisfaction problems using the loihi spiking neuromorphic processor. IEEE Design, Automation and Test in Europe, 2020, 1079-1084, Grenoble, France.
  • Alom, M. Z., Taha, T., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Essen, B., Awwal, A., & Asari, V. (2019, March). A state of the art survey on deep learning theory and architectures. MDPI Electronics, 8(3), 292. (Best Paper Award)
  • Alom, M. Z., Yakopcic, C., Nasrin, M. S., Taha, T. M., & Asari, V. K. (2019, August). Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. Journal of Digital Imaging, 32, 605-617.
  • Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. (2019, March). Recurrent residual U-Net (R2U-Net) for medical image segmentation. Journal of Medical Imaging, 6(1).
  • Bontupalli, V., Yakopcic, C., Hasan, R., & Taha, T. M. (2018, Dec.). Efficient memristor based architecture for intrusion detection and high-speed packet classification. ACM Journal on Emerging Technologies in Computing Systems (JETC) – Special Issue on Neuromorphic Computing, 14(4), 41:1-41:27.
  • Yakopcic, C., Wang, S., Wang, W., Shin, E., Boeckl, J., Subramanyam, G., & Taha, T. M. (2018, Dec.). Filament formation in lithium niobate memristors supports neuromorphic programming capability. Neural Computing and Applications, 30, (12), 3773-3779.
  • Yakopcic, C., Hasan, R., & Taha, T. M. (2018, Aug.). Flexible memristor based neuromorphic system for implementing multi-layer neural network algorithms. International Journal of Parallel, Emergent and Distributed Systems, 33(4), 408-429.
  • Yakopcic, C., Bontupalli, V., Hasan, R., Mountain, D., & Taha, T. M. (2017, March). Self-biasing memristor crossbar used for string matching and TCAM implementation. Electronics Letters, 53(7), 463-465.
  • Yakopcic, C., Taha, T. M., Subramanyam, G., & Pino, R. E. (2013, August). Memristor SPICE model and crossbar simulation with nanosecond switching time. IEEE/INNS International Joint Conference on Neural Networks (IJCNN), 1-7, Dallas, TX. (Best Paper Award)
  • Yakopcic, C., Taha, T. M., Subramanyam, G., & Pino, R. E. (2013, August). Generalized memristive device SPICE model and its application in circuit design. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 32(8), 1201-1214.
  • Yakopcic, C., Taha, T. M., Subramanyam, G., Pino, R. E. & Rogers, S. (2011, Oct.). A memristor device model, IEEE Electron Device Letters, 30(10), 1436-1438.

Full list of publications can be found here (https://cyakopcic1.wordpress.com/).

Selected Patents

  • M. Taha, R. Hasan, C. Yakopcic, On-chip training of memristor crossbar neuromorphic processing systems, U.S. 10,855,429, Jan. 5, 2021.
  • M. Taha and C. Yakopcic, Memristor crossbar configuration, U.S. 10,622,064, April 14, 2020.
  • Yakopcic, R. Hasan, T. M. Taha, Analog neuromorphic circuit implemented using resistive memories, U.S. 10,474,948, Nov. 12, 2019.
  • Yakopcic, T. M. Taha, and R. Hasan, Analog neuromorphic circuits for dot-product operation implementing resistive memories, U.S. Patent, US 10,176,425, Jan. 8, 2019.

Full list of publications can be found here (https://cyakopcic1.wordpress.com/).

Selected Honors and Awards

  • 2020 IEEE Dayton Section Computer Society Award
  • 2020 MDPI Electronics Journal Best Paper Award
  • 2013 IEEE/INNS International Joint Conference on Neural Networks Best Paper Award

Courses Taught

  • ECE 431L
  • ECE 432L

Degrees

  • Ph.D., Electrical Engineering, University of Dayton, 2014
  • M.S., Electrical Engineering, University of Dayton, 2011
  • B.S., Electrical Engineering, University of Dayton, 2009

Research Interests

  • Low Power Autonomous Systems
  • Deep Learning for Efficient and Portable Applications
  • Spiking Neural Network Processors
  • Memristor Based Neuromorphic Circuit Design
  • Memristor Device Modeling and Characterization