# Relationship Estimation Metrics for Binary SoC Data

## TL;DR

This paper compares various metrics for detecting relationships in long, complex SoC data vectors, highlighting the most accurate methods for understanding inter-component dependencies.

## Contribution

It empirically evaluates and compares the accuracy of different relationship estimation metrics on simulated SoC data with known relationships.

## Key findings

- Cov and Dep metrics are most effective for relationship detection
- Hamming distance and geometric metrics are less useful
- Probabilistic modeling enables systematic accuracy testing

## Abstract

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.