  System-on-Chip (SoC) designs are used in every aspect of computing and their
optimization is a difficult but essential task in today's competitive market.
Data taken from SoCs to achieve this is often characterised by very long
concurrent bit vectors which have unknown relationships to each other. This
paper explains and empirically compares the accuracy of several methods used to
detect the existence of these relationships in a wide range of systems. A
probabilistic model is used to construct and test a large number of SoC-like
systems with known relationships which are compared with the estimated
relationships to give accuracy scores. The metrics \.Cov and \.Dep based on
covariance and independence are demonstrated to be the most useful, whereas
metrics based on the Hamming distance and geometric approaches are shown to be
less useful for detecting the presence of relationships between SoC data.
