Presentation
Enabling AI Agent Flows for Verification Triage & Debug
DescriptionVerification remains the most time-consuming phase of hardware development, with millions of simulation jobs generating diverse fail signatures. Manual triage and root cause analysis (RCA) of these failures is a critical bottleneck, consuming significant engineering effort and requiring specialized expertise.
We present an AI-driven approach that leverages agentic workflows to automate and accelerate fail triage and debug. Our solution integrates heterogeneous verification context—design specifications, HDL, waveforms, coverage data, and prior issues—through Model Context Protocol (MCP) servers, enabling agents to retrieve and correlate information efficiently. Customized agents iteratively process simulation traces, extract cone-of-influence logic, and correlate transactions with waveform and coverage data. Deployed in IBM Z hardware verification, these flows have demonstrated substantial productivity improvements, estimated reduction of manual effort by 15-40% and enhanced overall verification throughput. This work also provides insights into MCP integration challenges such as context bloat and API granularity and outlines best practices for implementing AI-driven debug solutions in complex hardware environments.
We present an AI-driven approach that leverages agentic workflows to automate and accelerate fail triage and debug. Our solution integrates heterogeneous verification context—design specifications, HDL, waveforms, coverage data, and prior issues—through Model Context Protocol (MCP) servers, enabling agents to retrieve and correlate information efficiently. Customized agents iteratively process simulation traces, extract cone-of-influence logic, and correlate transactions with waveform and coverage data. Deployed in IBM Z hardware verification, these flows have demonstrated substantial productivity improvements, estimated reduction of manual effort by 15-40% and enhanced overall verification throughput. This work also provides insights into MCP integration challenges such as context bloat and API granularity and outlines best practices for implementing AI-driven debug solutions in complex hardware environments.
Event Type
Engineering Poster
TimeTuesday, July 285:00pm - 6:00pm PDT
LocationDAC Pavilion, Exhibit Floor