AutoFlows++: Hierarchical Message Flow Mining for System on Chip Designs

TL;DR

AutoFlows++ is a hierarchical framework that improves message flow mining accuracy in complex SoC communication traces by combining local and global analysis stages.

Contribution

It introduces a novel two-stage hierarchical approach that enhances scalability and accuracy in extracting communication behaviors from complex SoC traces.

Findings

AutoFlows++ outperforms prior methods in flow extraction accuracy.

The framework effectively handles complex interleaved message patterns.

Experimental results validate its practicality for SoC validation.

Abstract

Understanding communication behavior in modern system-on-chip (SoC) designs is critical for functional verification, performance analysis, and post-silicon debugging. Communication traces capture message exchanges among system components and provide valuable insights into system behavior. However, deriving concise communication specifications from such traces remains challenging due to interleaved instances of communication flows, and ambiguous causal relationships among messages. Existing mining approaches often struggle with scalability and ambiguity when traces contain complex interleaving of message patterns across multiple components. These conditions often lead to an explosion in the number of candidate flows and inaccurate extraction of communication behaviors. This paper presents AutoFlows++, a design-architecture-guided hierarchical framework for mining message flows from communication traces of complex SoC designs. AutoFlows++ operates in two stages: local mining followed by global mining. In the local mining stage, simple communication patterns are extracted from traces observed at individual communication interfaces between components. In the global mining stage, these local patterns are composed to identify higher-level message flows that characterize communication behavior across multiple components. Experimental results on both synthetic traces and traces generated from SoC models in GEM5 demonstrate that AutoFlows++ significantly improves flow extraction accuracy compared with prior approaches, highlighting its effectiveness for practical SoC validation tasks.