A standalone FPGA based emulated-digital CNN-UM system

Zsolt Vörösházi, András Kiss, Zoltán Nagy, Péter Szolgay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The Falcon emulated-digital CNN-UM (Cellular Neural/Nonlinear Networks Universal Machine) architecture has been extended by an embedded GAPU (Global Analogic Programming Unit) using the flexible Xilinx MicroBlaze soft-core processor to take full advantage of the joint computing power of high-speed distributed arithmetics and programmability. The implemented GAPU provides a stand-alone operation, which is capable of controlling complex sophisticated CNN analogic algorithms similar to various visual microprocessors, such as the ACE4k, ACE16k, and Bi-i vision systems. The quality of the embedded GAPU implementation is demonstrated by analogic algorithms, mainly in which sequences of template operations are required.

Original languageEnglish
Title of host publication2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures
Number of pages1
DOIs
Publication statusPublished - Sep 23 2008
Event2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures - Santiago de Compostela, Spain
Duration: Jul 14 2008Jul 16 2008

Publication series

NameProceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications

Other

Other2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures
CountrySpain
CitySantiago de Compostela
Period7/14/087/16/08

ASJC Scopus subject areas

  • Software

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  • Cite this

    Vörösházi, Z., Kiss, A., Nagy, Z., & Szolgay, P. (2008). A standalone FPGA based emulated-digital CNN-UM system. In 2008 11th International Workshop on Cellular Neural Networks and their Applications, CNNA 2008, Cellular Nano-scale Architectures [4588635] (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications). https://doi.org/10.1109/CNNA.2008.4588635