An FPGA implementation of GENET for solving graph coloring problems

Constraint satisfaction problems (CSPs) can be used to model problems in a wide variety of application areas, such as time-table scheduling, bandwidth allocation, and car-sequencing. To solve a CSP means finding appropriate values for its set of variables such that all of the specified constraints are satisfied. Almost all CSPs have exponential time complexity and instances of them may require a prohibitively large amount of time to solve. Consequently, much research has been done in developing efficient methods to solve CSPs. In particular, a generic neural network (GENET) model, developed by C.J. Wang and E.P.K. Tsang (1991), has been demonstrated to work extremely well in solving many CSPs, often finding solutions where other methods fail.