Binary neural networks for N-queens problems and their VLSI implementations

Nobuo Funabiki, Takakazu Kurokawa, Masaya Ohta

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Combinatorial optimization problems compose an important class of mathematical problems that include a variety of practical applications, such as VLSI design automation, communication network design and control, job scheduling, games, and genome informatics. These problems usually have a large number of variables to be solved. For example, problems for VLSI design automation require several million variables. Besides, their computational complexity is often intractable due to NP-hardness. Neural networks have provided elegant solutions as approximation algorithms to these hard problems due to their natural parallelism and their affinity to hardware realization. Particularly, binary neural networks have great potential to conform to current digital VLSI design technology, because any state and parameter in binary neural networks are expressed in a discrete fashion. This paper presents our studies on binary neural networks to the N-queens problem, and the three different approaches to VLSI implementations focusing on the efficient realization of the synaptic connection networks. Reconfigurable devices such as CPLDs and FPGAs contribute the realization of a scalable architecture with the ultra high speed of computation. Based on the proposed architecture, more than several thousands of binary neurons can be realized on one FPGA chip.

Original languageEnglish
Pages (from-to)271-296
Number of pages26
JournalControl and Cybernetics
Issue number2
Publication statusPublished - Dec 1 2002


  • Algorithm
  • Binary neural network
  • Combinatorial optimization
  • NP-hard
  • VLSI design

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Applied Mathematics


Dive into the research topics of 'Binary neural networks for N-queens problems and their VLSI implementations'. Together they form a unique fingerprint.

Cite this