Unifying algorithm/architecture for artificial neural networks

S. Y. Kung, J. N. Hwang

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations


A generic iterative model is presented for a wide variety of artificial neural networks (ANNs): single-layer feedback networks, multilayer feed-forward networks, hierarchical competitive networks, and hidden Markov models. Unifying mathematical formulations are provided for both the retrieving and learning phases of ANNs. Based on the unifying mathematical formulation, a programmable universal ring systolic array is derived for both phases. It maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on communication. Hardware implementation for the processing units based on CORDIC techniques is discussed.

Original languageEnglish (US)
Pages (from-to)2505-2508
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 1989
Event1989 International Conference on Acoustics, Speech, and Signal Processing - Glasgow, Scotland
Duration: May 23 1989May 26 1989

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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