Modeling temporal uncertainty in microprocessor systems

Abstract

In microprocessor system diagnosis, temporal reasoning of event changes occurring at imprecisely known time instants is an important issue. The time range approach was proposed to capture the notion of time imprecision in event occurrence. According to this concept, efficient time range constraint reasoning techniques were developed for embedding domain knowledge in a deep level constraint model. The imprecision in these events contributes to a certain degree of uncertainty in the correctness of a microprocessor system operation. A knowledge based diagnostic system for microprocessor systems design was designed and developed. The system performs worst case timing analysis. In particular, for the asynchronous bus operation of the MC68000 microprocessor, the sequence of events during a read cycle was traced through an inference process to determine if any constraint in the model was violated Although satisfactory results were obtained, the possibility measures implicitly embedded within time ranges were not properly quantified for effective temporal reasoning. To overcome this shortcoming, the fuzzy time point model is proposed. The original time range representation, specified by two crisp interval end points, is replaced by the fuzzy time point representation that is specified by a single fuzzy value. The degree of fuzziness of a fuzzy time point has dependency on the functional specification of the corresponding timing parameter. The use of simplistic assumptions on the fuzzy time point model has been shown to enhance the deductive capability of the existing time range models.