Ever-shrinking time-to-market windows, geographically diverse teams, and the wide range of design variables have made it difficult to track and optimize the design workflow. This gives teams limited visibility of the design process, adding inefficiencies to the team’s productivity and systemic risk throughout.
As Karl Freund at Forbes noted, “Every chip design team has probably watched this movie before: after months of hard work designing a new product, the team has suddenly lost the recipe, and the latest simulation run veers from the steady progress the team has been making towards tape-out. What went wrong? And how can the team quickly recover to stay on schedule?“
Another issue that organizations face is the loss of knowledge when people and teams inevitably change. In a conversation with Kalar Rajendiran at SemiWiki, Richards alluded that keeping note of various observational learnings from one project is not only helpful at the start of projects, but is valuable when facing problems that need to be debugged. This is where and why institutional knowledge developed over time plays an important role.
“Everything is fine until one or more team members leave and/or when many fresh engineers start working on a project. One of the biggest gripes at many companies is the loss of talent along with institutional learnings,” writes Rajendiran.
Speaking to Spencer Chin at Design News, Richards examined how design engineers have tried to get around the problem of methodologically understanding complex data by resorting to time-consuming solutions to track data. With limited insights on the depth and breadth of the data at hand, this approach only hurts productivity in the long run.
“We want to accelerate the design debug and optimization process. The design process has long been opaque, with designers and managers typically having a limited view of the entire design process, making it difficult to track and improve. There are a number of gauges inside a design, including power, clock rate, etc. All of these variables are interconnected and affect one another.”
To keep up with the pace of changing customer requirements amid the growing necessity for “actionable” data-driven insights, the semiconductor industry needs a new flow of tools to design more efficiently and aid decision-making as we move into the era of SysMoore.