Implementation of cellular learning automata on reconfigurable computing systems
Morteza Saheb Zamani, Farhad Mehdipour, Mohamad Reza Meybodi · 2004
Reconfigurable computing systems (RCS) use the flexibility of programmable devices and the speed of hardware to implement high performance systems. Implementation of RCS is normally made by means of programmable devices, such as FPGAs. On the other hand, recently, cellular learning automata (CLA) have been proposed as a combination of conventional cellular automaton and learning automaton. Software simulation of CLA has shown it to be successful for solving some hard problems. However, the process on conventional computers is slow. To overcome this problem, we implemented CLA in hardware. In addition, for some applications which necessitate run time changes for parameters, the ability of run-time reconfiguration (RTR) in hardware is a solution. In this paper, the design and implementation of CLA on a reconfigurable system are presented. Experimental results show considerable speedup gain of RCS version over the software version. Independence on CLA dimensions is another benefit of reconfigurable hardware implementation of CLA. In other words, by increasing the dimensions of CLA, the time needed for running reconfigurable CLA implemented on hardware remains constant.