An Assistant to Ease Your Transition to PSS

At times it has seemed like any development in EDA had to build a GenAI app that would catch the attention of Wall Street. Now I see more attention to GenAI being used for less glamorous but eminently more practical advances. This recent white paper from Siemens on how to help verification engineers get up to speed faster with PSS is a good example of a trend that uses GenAI to enhance engineering productivity in complex flows, rather than upending flows. While revolutionary new methods may continue to excite, these more modest advances will pay off in the short term and may ultimately be more durable.

Verification intent and the tension between PSS and UVM

A powerful way to enhance productivity is to work directly with high-level intent, in this case verification tests, rather than implementation, assuming you have a way to generate the implementation from that intent. UVM is the default representation for test intent today, but its intent is entangled with UVM implementation details.

PSS, on the other hand, is very good at representing high-level intent, rather than implementation, and can directly generate UVM and C testbenches to drive standard DV flows. But PSS is less familiar to DV engineers who have already invested in learning their way around UVM features and dialects and have little time to learn new approaches.

Does the methodology even need to change? Unfortunately designs continue to get more complex, and DV engineers must continue to move with the times, just like everyone else. But it’s not unreasonable for them to expect help in making that transition. This where Questa One’s Portable Stimulus Assist becomes useful, guiding PSS novices to build their own PSS models through natural language prompts.

Why not use GenAI to assist UVM generation?

Good question. A GenAI assistant could cut out a PSS middleman and go straight to generating UVM. However, the author of the whitepaper has a detailed answer for why this is not the best approach, which reinforces a suspicion I have about most effective uses of GenAI technology: that GenAI models often perform best when the expression gap between the initial request/prompt and deliverable is not too wide.

I see this also in spec refinement tools and in modern prompt guidance tools. When the output is still reasonably close to intent, it is easier for us to spot and correct mistakes. But if the tool must cross a wider gap, going straight to implementation, it is harder for us to spot where it may have gone wrong, especially for subtle mistakes.

A related problem is that crossing wider gaps with confidence depends on more extensive training corpora. There are many possible ways to implement a piece of intent. Few of these would probably meet best design practices, but without guided fine-tuning in training there is no reason to expect those best practices will necessarily be honored.

In contrast, developing a PSS model starting from a prompt should be much simpler since it will be easier for a DV engineer to check and refine intent in the PSS model against expectations. And once captured and approved in PSS, translation to a UVM or other model is pushbutton, because that capability is already built into PSS tools and libraries.

This white paper details specific examples of why a direct GenAI to UVM generation would be challenging.

Nice paper and a very practical application. The link to the paper is HERE.