AI-Augmented Verification for Next-Generation VLSI Systems: Challenges, Techniques and Future Directions

Abstract

The verification problems in the semiconductor industry have never been more of a challenge as the System-on-Chip architectures bring in heterogeneous computing units, sophisticated memory hierarchies and various interface standards into an ever-more complex silicon implementation. Design verification has become the most popular bottleneck in semiconductor development, consuming large amounts of engineering resources and failing to cope with the exponentially growing state-space and protocol diversity. Artificial Intelligence can provide a groundbreaking direction with the possibility to automate the verification processes with scalable, data-driven solutions. Machine learning is an effective system in the extraction of patterns within huge simulation logs, coverage databases and regression histories that go unexploited during traditional processing. Early applications such as intelligent test generation with reinforcement learning, automated debug assisting with failure clustering and waveform pattern recognition, coverage optimization with predictive analytics and natural language processing with verification planning have shown verifiable improvements in verification efficiency. The combination of advanced machine learning algorithms, convenient compute infrastructure in the cloud and the availability of extensive verification data sets places AI as a real leverage of modern verification systems as an extension layer. Nevertheless, in order to achieve full potential, it is necessary to overcome issues associated with the quality of data, interpretability of the models, integration with the existing UVM and formal verification flows and cross-design generalization. The trend is to AI-native verification ecosystems where engineers are no longer engaged in manual test development but only in managing intelligent automation processes.