Blending methodologies for optimizing fuzzy inference engine designs

The software paradigm of writing sequential programming tasks executed on a single processor has pervaded computers since their dawning. In spite of progress, sequential execution of certain algorithms remains limited by this paradigm. In particular, fuzzy control systems involve fuzzy set operations, which require significant amounts of vector and matrix computation. This computation can be considered an inherently parallel task, and performing these operations in software results in inefficient execution, severely limiting the use of fuzzy operations in real-time systems where fast responses are required. This paper would explore solutions to this problem using software, hardware and finally a hybrid approach with a proposed computing architecture and platform known as a granular computer (GC).