Tarek M. Taha


Full-Time Faculty


Email: Tarek Taha
Phone: 937-229-3119
Kettering Laboratories Room 241B

Parallel Cognitive Systems Laboratory

At the University of Dayton, Dr. Taha leads the Parallel Cognitive Systems Laboratory. The lab is specialized in biologically inspired computing systems (neuromorphic computing), high performance parallel computing, and the interface between these two areas. There is a strong emphasis to apply the research to autonomous systems.

In neuromorphic computing, the lab is examining all aspects of designing these systems including:

  • Memristor based mixed signal circuits for learning in neural networks
  • Multicore neuromorphic computing architectures (both digital and mixed signal)
  • The application of these architectures
  • Fabrication and modeling of memristor devices
  • New algorithms for deep learning

In high performance parallel computing, the lab is examining:

  • Map applications and algorithms to parallel computing architectures
  • FPGA implementation of algorithm
  • The use of neuromorphic computing in high performance systems
  • Develop high performance parallel algorithms for cognitive agents


Selected Publications

  • Hasan, R., & Taha, T. M. (2017, May). On-chip training of memristor-based, deep neural networks. IEEE International Joint Conference on Neural Networks.
  • Atahary, T., Taha, T. M., & Douglass, S. (2016, June). Parallelized mining of domain knowledge on GPGPU and Xeon Phi clusters. The Journal of Supercomputing, 72(6), 2132-2156.
  • Yakopcic, C., Alom, Z., & Taha, T. (2017, May). Extremely parallel memristor crossbar architecture for convolutional neural network implementation. IEEE International Joint Conference on Neural Networks.
  • Hasan, R., Taha, T. M., Yakopcic, C., & Mountain, D. (2016, November). High throughput neural network based embedded streaming multicore processors. IEEE International Conference on Rebooting Computing (ICRC), San Diego, California.

Click here to go to my Google Scholar page for a list of all of my publications and citations.

Research and Work

  • CAREER: Scalable computer architectures of hierarchical neocortex models and K-12 education enhancement, National Science Foundation
  • Investigation of large scale cortical models on clustered multicore processors, Air Force Office of Scientific Research
  • Investigating node design and training of neuromorphic computing architectures of the neocortex, Air Force Research Laboratory

Honors and Awards

  • NSF CAREER Award, 2007
  • Clemson University Board of Trustees Award for Faculty Excellence, 2008
  • Eta Kappa Nu, 1999
  • Phi Beta Kappa, 1994

Courses Taught

  • Fundamentals of Computer Architecture (ECE 314)
  • Computer Design (ECE 533)
  • Advanced Computer Architecture (ECE 636)


  • Ph.D., Electrical Engineering, Georgia Institute of Technology, 2002
  • M.S.E.E, Electrical Engineering, Georgia Institute of Technology, 1998
  • B.S.E.E, Electrical Engineering, Georgia Institute of Technology, 1996
  • B.A., Pre-Engineering, DePauw University, 1996

Research Interests

  • Computer architecture
  • High performance computing
  • High throughput neuromorphic computing
  • Memristor based architectures
  • Reconfigurable computing
  • Processor performance prediction