Time and space efficient method for accurate computation of error detection probabilities in VLSI circuits

The authors propose a novel fault/error model based on a graphical probabilistic framework. They arrive at the logic induced fault encoded directed acrylic graph (LIFE-DAG), which is proven to be a Bayesian network, capturing all spatial dependencies induced by the circuit logic. Bayesian networks are the minimal and exact representation of the joint probability distribution of the underlying probabilistic dependencies that not only use conditional independencies in modelling but also exploit them for achieving minimality and smart probabilistic inference. The detection probabilities also act as a measure of soft error susceptibility (an increased threat in the nano-domain logic block) which depends on the structural correlations of the internal nodes and also on input patterns. Based on this model, they show that they are able to estimate detection probabilities of faults/errors on ISCAS´85 benchmarks with high accuracy, linear space requirement complexity, and with an order of magnitude (≈5 times) reduction in estimation time over corresponding binary decision diagram based approaches.