FPGA based implementation of a Hopfield neural network for solving constraint satisfaction problems

The paper discusses the implementation of Hopfield neural networks for solving constraint satisfaction problems using field programmable gate arrays (FPGAs). It discusses techniques for formulating such problems as discrete neural networks, and then it describes the N-Queen problem using this formulation. A prototype implementation of a number of different N-Queen problems is described and results are presented that illustrate that a speedup of up to 3 orders of magnitude is possible using current FPGA devices.