Presentation
Taming the Beast: A Formal Verification Approach to Solve Design Complexity- GPU Compression Block Case Study
DescriptionFormal verification plays a critical role in modern hardware development by delivering exhaustive, mathematically rigorous validation to ensure functional correctness. As digital architectures scale in complexity—driven by advanced features and the growing demand for AI centric workloads—formal verification faces major hurdles, including state space explosion and difficulty achieving convergence. GPU compression blocks exemplify this trend: originally designed for efficient data storage and bandwidth reduction, these blocks have evolved to include intricate control and data path logic, multi-cache line handling, and interdependent compression/decompression flows. The addition of AI-driven features has further increased gate counts and buffer sizes, amplifying verification complexity.
To address these challenges, we implemented a structured formal verification methodology which is scalable for complex designs. Our approach divides the system into smaller, focused blocks, applies abstraction and parameter reduction to manage state space, leverages incremental verification and symbolic coding to deal with complexity. These techniques enabled us to dramatically improve convergence rates from under 6% to nearly 83% and uncover critical issues that would be difficult to detect without these advanced techniques or simulation. This approach delivers a scalable, resilient verification framework that strengthens GPU architecture reliability while enabling secure, efficient processing for advanced AI driven workloads in future systems.
To address these challenges, we implemented a structured formal verification methodology which is scalable for complex designs. Our approach divides the system into smaller, focused blocks, applies abstraction and parameter reduction to manage state space, leverages incremental verification and symbolic coding to deal with complexity. These techniques enabled us to dramatically improve convergence rates from under 6% to nearly 83% and uncover critical issues that would be difficult to detect without these advanced techniques or simulation. This approach delivers a scalable, resilient verification framework that strengthens GPU architecture reliability while enabling secure, efficient processing for advanced AI driven workloads in future systems.
Event Type
Engineering Presentation
TimeTuesday, July 284:15pm - 4:30pm PDT
LocationSeaside Ballroom A