AI for VLSI · All levels
AI-VLSI Metrics Reference
Common metrics for model quality, hardware efficiency, and workflow deployment confidence.
Core metric families
Model quality: loss, task metric, calibration error, drift score.
Training efficiency: convergence rate, compute cost, scaling efficiency.
Hardware efficiency: utilization, bandwidth use, latency, perf-per-watt.
Workflow impact: runtime savings, signoff-correlation quality, false recommendation rate.
Deployment safety: compatibility pass rate, rollback readiness, incident SLA.
diagram
BEFORE / AFTER — AI-VLSI Closure
metric quality
^
| o target region
| o post-fix + regression
| o
| o baseline failing run
+-----------------------------------------> iteration
evidence audit fix full validation