The design and analysis of a CMOS low-power large-neighborhood CNN with propagating connections

Chung-Yu Wu*, Sheng Hao Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The design of a large-neighborhood cellular nonlinear network (LN-CNN) with propagating connections is proposed. The propagating connections are utilized to achieve large-neighborhood templates in the shape of diamonds. Based on the propagating connections, each LN-CNN cell can only be connected to neighboring cells without interconnections to farther cells. Thus, it is suitable for very large scale integration implementation. The LN-CNN functions of diffusion, deblurring, and Müller-Lyer illusion are successfully verified. Meanwhile, the functions of erosion and dilation are expanded with the diamond-shaped LN templates. Furthermore, the simple N- and P-type synapses stop all the static current paths so that the dc power dissipation can be reduced to only 0.7 mW on standby and 18 mW in operation. An experimental LN-CNN chip with a 20 × 20 array has been fabricated using 0.18-μ CMOS technology. With the proposed LN-CNN chip, more applications and LN-CNN templates can be studied further.

Original languageEnglish
Pages (from-to)440-452
Number of pages13
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume56
Issue number2
DOIs
StatePublished - 4 Mar 2009

Keywords

  • Cellular neural networks (CNNs)
  • CMOS
  • Large-neighborhood (LN)
  • Propagating connections

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