Abstract:
This proposal outlines the utilization of AI and ML algorithms within the UVM framework
to enhance the stimulus selection process in design verification. With the exponential
growth in system complexity, efficient verification methodologies are essential.
The proposed approach addresses this challenge by employing AI techniques, such as
K-means clustering, to identify the most effective stimuli from a set of multiple stimuli
for a specific design within the UVM framework. By analyzing the design’s characteristics
and the stimuli’s impact, the AI system will intelligently select and generate
stimuli, thereby improving verification efficiency. The study focuses on developing AI
models tailored for this task and evaluating their effectiveness in reducing verification
time and resources while ensuring comprehensive coverage. By implementing the AI and
ML model, we observed a 49% improvement in transactions and a significant reduction
in the time required to achieve 100% functional coverage.
