Time and Space Efficient Method for Accurate Computation of Error Detection Probabilities in VLSI Circuits

We propose a novel fault/error model based on a graphical probabilistic framework. We arrive at the Logic Induced Fault Encoded Directed Acyclic Graph (LIFE-DAG) that 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 modeling but also exploits them for achieving minimality and smart probabilistic inference. The detection probabilities also act as a measure of soft error susceptibility (an increased threat in nano-domain logic block) that depends on the structural correlations of the internal nodes and also on input patterns. Based on this model, we show that we 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 BDD based approaches.