Presentation
Portfolio Re-characterization using AI
DescriptionCell library characterization is often a difficult and time-consuming task, due to the breadth of calculations and simulations required in its process. In typical characterization runs, simulations can be in the billions, resulting in days to weeks for completion. This is exemplified further for standard cell libraries with large sets of PVTs and thousands of cells, further increasing the engineering resources, computational consumption, and project time.
Recently, advances and developments in EDA technology have allowed for the use of AI algorithms to reduce the overall time committed to a characterization flow. In this paper, we'll discuss a 2-step methodology known as Portfolio Re-Characterization to characterize and generate data in .libs using AI.
The methodology in question first identifies seed PVTs among the total set of required PVTs for use in full scale characterization through reinforcement learning. Next, the same seed PVTs are used as training data in an AI-enabled workflow to produce new .libs without the need for full characterization. Through this approach, standard cell library characterization runtime and resources are reduced.
Recently, advances and developments in EDA technology have allowed for the use of AI algorithms to reduce the overall time committed to a characterization flow. In this paper, we'll discuss a 2-step methodology known as Portfolio Re-Characterization to characterize and generate data in .libs using AI.
The methodology in question first identifies seed PVTs among the total set of required PVTs for use in full scale characterization through reinforcement learning. Next, the same seed PVTs are used as training data in an AI-enabled workflow to produce new .libs without the need for full characterization. Through this approach, standard cell library characterization runtime and resources are reduced.
Event Type
Engineering Poster
TimeTuesday, June 245:00pm - 6:00pm PDT
LocationEngineering Posters, Level 2 Exhibit Hall