Hidden Markov modeling and fuzzy controllers in FPGAs

This paper compares software and FPGA-based hardware implementations of two applications. The first application uses hidden Markov models, and the second application is a fuzzy controller. Hidden Markov modeling is used for temporal pattern recognition and speech recognition in particular. Both applications are accelerated when implemented in FPGA-based hardware, but this acceleration is obtained by using different algorithms than those used in software implementations. These different algorithms produce slightly different outputs; therefore both solution quality and performance must be evaluated to compare hardware and software implementations. The experience of designing these applications has implications for hardware/software codesign tools and for the migration of existing software applications to FPGA-based hardware.