Presentation
CHIPAGENTS AI: Autonomous Root Cause Analysis: Agentic AI for Debugging at Commercial Scale
DescriptionOver half of frontend ASIC engineering time is spent on debugging and root cause analysis, navigating millions of lines of HDL and terabytes of waveform data. Despite this cost, hardware debugging remains almost entirely manual. This presentation introduces ChipAgents RCA, the first autonomous, agentic AI system for end-to-end ASIC root cause analysis using both code and waveform data at commercial scale.
ChipAgents RCA treats debugging as a structured search problem. The system combines three core innovations: (1) a waveform understanding engine purpose-built for AI agents that enables symbolic, query-based reasoning over massive waveform databases; (2) a novel multi-agent prover-verifier architecture that explores debugging hypotheses in parallel while enforcing skepticism and verification; and (3) a self-consistency ranking layer that calibrates confidence and surfaces the most reliable explanations to engineers.
The system has been evaluated across a diverse dataset of commercial-scale IPs, including bus fabrics, RISC-V cores, and complex protocols such as PCIe and DDR, spanning bug classes like backpressure, data corruption, protocol violations, and clock-domain issues. ChipAgents RCA achieves over 3x higher pass-at-one accuracy than state-of-the-art generic AI agents. In a representative PCIe 3.0 case study (36k lines of code, multi-level indirection), ChipAgents RCA isolated the exact root cause and patch in 10 minutes, compared to 4–8 hours of projected human effort, representing a 12x speedup.
This talk will present the system architecture, evaluation results, and lessons learned deploying autonomous debugging agents in real verification flows, highlighting how agentic AI can fundamentally change how hardware teams approach debug and verification closure.
ChipAgents RCA treats debugging as a structured search problem. The system combines three core innovations: (1) a waveform understanding engine purpose-built for AI agents that enables symbolic, query-based reasoning over massive waveform databases; (2) a novel multi-agent prover-verifier architecture that explores debugging hypotheses in parallel while enforcing skepticism and verification; and (3) a self-consistency ranking layer that calibrates confidence and surfaces the most reliable explanations to engineers.
The system has been evaluated across a diverse dataset of commercial-scale IPs, including bus fabrics, RISC-V cores, and complex protocols such as PCIe and DDR, spanning bug classes like backpressure, data corruption, protocol violations, and clock-domain issues. ChipAgents RCA achieves over 3x higher pass-at-one accuracy than state-of-the-art generic AI agents. In a representative PCIe 3.0 case study (36k lines of code, multi-level indirection), ChipAgents RCA isolated the exact root cause and patch in 10 minutes, compared to 4–8 hours of projected human effort, representing a 12x speedup.
This talk will present the system architecture, evaluation results, and lessons learned deploying autonomous debugging agents in real verification flows, highlighting how agentic AI can fundamentally change how hardware teams approach debug and verification closure.
Event Type
Exhibitor Forum
TimeMonday, July 271:45pm - 2:15pm PDT
LocationExhibitor Forum, Exhibit Floor