Parallel fuzzy inference on hypercube computer

In fuzzy database systems that have very large rules or fuzzy data, the inference time is much increased. Therefore, a high performance parallel fuzzy inference architecture is needed. In this paper, we propose a novel parallel fuzzy inference engine using Hypercube architecture. In this, fuzzy rules are distributed and executed simultaneously. The ONE-TO-ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL-TO-ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This architecture can be used in large expert systems or fuzzy database systems.