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FPGA Design of a Multicore Neuromorphic Processing System

Zhang, Bin
http://orcid.org/0000-0001-6551-7869
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1461694994

Abstract Details

2016, Master of Science (M.S.), University of Dayton, Electrical Engineering.
Neuromorphic computing architecture has developed rapidly during recent years. Neuronmorphic network processor FPGA implementation is 3x and 127x faster than Intel E8400 processor with edge detection applications and ECG applications respectively. Considering resource utilization and system stability, a hardware-controlled communication routing network is a better choice than a time-delay based routing network. The separation of data lines prevents the hardware-controlled communication routing network from turning into a large network.
Tarek Taha (Advisor)
Keigo Hirakawa (Committee Member)
Eric Balster (Committee Member)
27 p.
Electrical Engineering
Neuronmorphic Network; micro-processor; FPGA; ultra-low power processor

Recommended Citations

Citations

  • Zhang, B. (2016). FPGA Design of a Multicore Neuromorphic Processing System [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1461694994

    APA Style (7th edition)

  • Zhang, Bin. FPGA Design of a Multicore Neuromorphic Processing System. 2016. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1461694994.

    MLA Style (8th edition)

  • Zhang, Bin. "FPGA Design of a Multicore Neuromorphic Processing System." Master's thesis, University of Dayton, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1461694994

    Chicago Manual of Style (17th edition)

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This open access ETD is published by University of Dayton and OhioLINK.