Presentation
MAGCS: Multi-Agent Guided Configuration Search for Optimization Fault Detection in Logic Synthesis
DescriptionLogic synthesis tools are crucial to translate high-level descriptions into optimized gate-level netlists. However, complex optimization operations and operation configurations can cause synthesis faults. To address this, we propose MAGCS, a fault detection method using multi-agent reinforcement learning to dynamically refine optimization sequences. MAGCS consists of three components: a test program selector that applies feature extraction and cosine similarity to curate diverse test programs, an optimization selector using the A2C algorithm to adaptively adjust operations and configurations, and an optimization fault verifier performing equivalence checks to pinpoint optimization-induced faults. Using MAGCS, we identified 32 confirmed faults on Vivado and Yosys, all of which are resolved. MAGCS received recognition from the Vivado community for its significant contributions to tool improvement.
Event Type
Research Manuscript
TimeWednesday, June 255:00pm - 5:15pm PDT
Location3003, Level 3
EDA
EDA2: Design Verification and Validation