A DTCNN universal machine based on highly parallel 2-d cellular automata CAM 2

Takeshi Ikenaga*, Takeshi Ogura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


The discrete-time cellular neural network (DTCNN) is a promising computer paradigm that fuses artificial neural networks with the concept of cellular automaton (CA) and has many applications to pixel-level image processing. Although some architectures have been proposed for processing DTCNN, there are no compact, practical computers that can process real-world images of several hundred thousand pixels at video rates. So, in spite of its great potential, DTCNN's are not being used for image processing outside the laboratory. This paper proposes a DTCNN processing method based on a highly parallel two-dimensional (2-D) cellular automata called CAM 2. CAM 2 can attain pixel-order parallelism on a single PC board because it is composed of a content addressable memory (CAM), which makes it possible to embed enormous numbers of processing elements, corresponding to CA cells, onto one VLSI chip. A new mapping method utilizes maskable search and parallel and partial write commands of CAM 2 to enable high-performance DTCNN processing. Evaluation results show that, on average, CAM 2 can perform one transition for various DTCNN templates in about 12 microseconds. And it can perform practical image processing through a combination of DTCNN's and other CA-based algorithms. CAM 2 is a promising platform for processing DTCNN.

Original languageEnglish
Pages (from-to)538-546
Number of pages9
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Issue number5
Publication statusPublished - 1998
Externally publishedYes


  • Cellular automaton
  • Content addressable memory
  • Discrete-time cellular neural network
  • Real-time image processing
  • Table lookup multiplication

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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