Presentation
Accelerating Memory Cache Verification with AI-Powered Batch Flow Methodology
DescriptionAchieving rigorous 6-6.5σ yield targets in L1 caches and other memory IP components requires extensive high-sigma verification. However, traditional brute-force Monte Carlo simulations and manually piloted GUI-based analyses are too slow and inefficient to verify large numbers of cells within tight project timelines, limiting analysis scope and compromising critical worst-case corner identification.
This paper introduces an innovative AI-powered batch flow that automates and significantly accelerates high-sigma memory cache verification, ensuring accurate, full-coverage analysis across all cells and components. The proposed methodology leverages adaptive AI to rapidly identify worst-case corners and eliminates unnecessary high-sigma runs by only deploying brute-force accurate verification on the identified critical corners.
In an example testcase, this AI-powered batch flow achieved full-coverage verification across 726 netlists in 38 hours and 20 minutes, averaging 2,514 simulations per job, representing an average 3.25x runtime speedup and 2.4x simulation speedup per job over the previous GUI-based method. Enhanced modeling and yield solver algorithms also contributed to reducing per-job simulations, and the flow automation and parallelization reduced engineering effort and overall runtime, making full coverage verification feasible within production timelines and improving disk space management. This scalable, AI-driven flow provides a fast, accurate, and comprehensive solution for memory IP validation challenges.
This paper introduces an innovative AI-powered batch flow that automates and significantly accelerates high-sigma memory cache verification, ensuring accurate, full-coverage analysis across all cells and components. The proposed methodology leverages adaptive AI to rapidly identify worst-case corners and eliminates unnecessary high-sigma runs by only deploying brute-force accurate verification on the identified critical corners.
In an example testcase, this AI-powered batch flow achieved full-coverage verification across 726 netlists in 38 hours and 20 minutes, averaging 2,514 simulations per job, representing an average 3.25x runtime speedup and 2.4x simulation speedup per job over the previous GUI-based method. Enhanced modeling and yield solver algorithms also contributed to reducing per-job simulations, and the flow automation and parallelization reduced engineering effort and overall runtime, making full coverage verification feasible within production timelines and improving disk space management. This scalable, AI-driven flow provides a fast, accurate, and comprehensive solution for memory IP validation challenges.
Event Type
Engineering Poster
Engineering Presentation
TimeWednesday, July 293:00pm - 3:45pm PDT
LocationDAC Pavilion, Exhibit Floor