Microcode optimization with neural networks

Sunil Bharitkar, Kazuhiro Tsuchiya, Yoshiyasu Takefuji

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Microcode optimization is an NP-complete combinatorial optimization problem. This paper proposes a new method based on the Hopfield neural network for optimizing the wordwidth in the control memory of a microprogrammed digital computer. We present two methodologies, viz., the maximum clique approach, and a cost function based method to minimize an objective function. The maximum clique approach albeit being near O(1) in complexity, is limited in its use for small problem sizes, since it only partitions the data based on the compatibility between the microoperations, and does not minimize the cost function. We thereby use this approach to condition the data initially (to form compatibility classes), and then use the proposed second method to optimize on the cost function. The latter method is then able to discover better solutions than other schemes for the benchmark data set.

Original languageEnglish
Pages (from-to)698-703
Number of pages6
JournalIEEE Transactions on Neural Networks
Issue number3
Publication statusPublished - 1999
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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