Relationship Detection Measures for Binary SoC Data
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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 parallel streams of binary data 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 measures Cov() and Dep() based on covariance and independence are demonstrated to be the most useful, whereas measures based on the Hamming distance and geometric approaches are shown to be less useful for detecting the presence of relationships between SoC data.
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