Implementation of embedded emulated-digital CNN-UM global analogic programming unit on FPGA and its application

Zsolt Vörösházi, András Kiss, Zoltan Nagy, Peter Szolgay

Research output: Contribution to journalArticle

26 Citations (Scopus)


The paper addresses the issue of implementing an embedded global analogic programming unit (GAPU) on the reconfigurable emulated-digital cellular neural/nonlinear networks universal machine (CNN-UM) architecture that has been extended by a flexible Xilinx MicroBlaze soft processor core 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 an analogic algorithm, in which sequences of template operations are required. Based on the experiments, several important issues relating to the acceleration efficiency, accuracy, cell size, and area consumption are discussed and compared with different CNN-UM implementations.

Original languageEnglish
Pages (from-to)589-603
Number of pages15
JournalInternational Journal of Circuit Theory and Applications
Issue number5-6
Publication statusPublished - Jul 1 2008



  • Analogic algorithm
  • CNN-UM
  • Embedded GAPU
  • FPGA
  • MicroBlaze soft processor core

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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