One-dimensional discrete-time CNN with multiplexed template-hardware

G. Manganaro, J. Pineda de Gyvez

    Research output: Contribution to journalArticleAcademicpeer-review

    10 Citations (Scopus)
    126 Downloads (Pure)

    Abstract

    This paper presents a novel discrete-time and fully programmable cellular neural network (CNN) suitable for processing one-dimensional (1-D) signals. As 1-D signals are typically very long sequences, the system consists of a linear analog shift register for data I/O coupled to a 1×n CNN array. In addition to the 1-D CNN architecture, a unique feature of our implementation is that the number of multipliers needed to implement both CNN templates has been minimized. This is conceivable because the multipliers are multiplexed between the A*y and B*u products during alternating phases of the controlling clock. The CNN system has been implemented in current mode based on the S2I technique using MOSIS Orbit 2 µm CMOS technology. The paper presents a thorough behavioral analysis of the new architecture, circuit-level implementations, and corresponding measured experimental results
    Original languageEnglish
    Pages (from-to)764-769
    Number of pages6
    JournalIEEE Transactions on Circuits and Systems. I, Fundamental Theory and Applications
    Volume47
    Issue number5
    DOIs
    Publication statusPublished - 2000

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