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Optimum Microarchitectures for Neuromorphic Algorithms

Wang, Shu
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1323706763

Abstract Details

2011, Master of Science (M.S.), University of Dayton, Electrical Engineering.
At present there is a strong interest in the research community to develop large scale implementations of neuromorphic algorithms. These systems consume significant amounts of power, area, and are very expensive to build. This thesis examines the design space of multicore processors for accelerating neuromorphic algorithms. A new multicore chip will enable more efficient design of large scale neuromorphic computing systems. The algorithms examined in this thesis are the HMAX and Izhikevich models. HMAX was developed recently at MIT to model the visual system of the human brain. The Izhikevich model was presented by Izhikevich as a biologically accurate spiking neuron model. This thesis also examines the parallelization of the HMAX model for studying multicore architectures. The results show the best single core architectures for HMAX and Izhikevich are almost same, though HMAX needs more cache. The multicore study shows that the off chip memory bus width and physical memory latency could improve the performance of the multicore system.
Tarek M. Taha (Committee Chair)
Eric J. Balster (Committee Member)
Vijayan K. Asari (Committee Member)
41 p.
Electrical Engineering
neuromorphic; HMAX; Izhikevich; single core; multicore

Recommended Citations

Citations

  • Wang, S. (2011). Optimum Microarchitectures for Neuromorphic Algorithms [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1323706763

    APA Style (7th edition)

  • Wang, Shu. Optimum Microarchitectures for Neuromorphic Algorithms. 2011. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1323706763.

    MLA Style (8th edition)

  • Wang, Shu. "Optimum Microarchitectures for Neuromorphic Algorithms." Master's thesis, University of Dayton, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1323706763

    Chicago Manual of Style (17th edition)

dayton1323706763
664
© 2011, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.