Presentation
Auto Grouping And Improvement Of IR Critical Regions Using Unsupervised Learning
DescriptionIn our 2.5D/3D System on Chip (SoC) designs that are being developed at lower (< 10nm) technology nodes, it is crucial to ensure that the IR drop is within the signoff threshold limits in order to achieve the targeted PPA goals.
Traditionally this includes multiple iterations of IR simulations after which the engineer identifies the IR, timing critical areas in the design that need to be improved. Manual identification of even a handful of regions pose a significant bandwidth impact.
Utilizing k-means clustering algorithm, we have developed an end to end pipeline where the engineer can:
• Provide the IR threshold limit and the algorithm will provide the list of regions where instances having drop higher than the threshold are clustered.
• Provide the type of cell which is resistance critical and the algorithm will provide the list of regions where the instances of the specified cell type are clustered. (example: Level Shifters)
• Provide the Instance toggle rate data and algorithm will cluster the regions based on use given high toggle rate threshold.
The regions are provided in the form of bounding boxes which can then be incorporated into the PnR flows like PG grid reinforcement, VT swap to downsize cells, etc.
Traditionally this includes multiple iterations of IR simulations after which the engineer identifies the IR, timing critical areas in the design that need to be improved. Manual identification of even a handful of regions pose a significant bandwidth impact.
Utilizing k-means clustering algorithm, we have developed an end to end pipeline where the engineer can:
• Provide the IR threshold limit and the algorithm will provide the list of regions where instances having drop higher than the threshold are clustered.
• Provide the type of cell which is resistance critical and the algorithm will provide the list of regions where the instances of the specified cell type are clustered. (example: Level Shifters)
• Provide the Instance toggle rate data and algorithm will cluster the regions based on use given high toggle rate threshold.
The regions are provided in the form of bounding boxes which can then be incorporated into the PnR flows like PG grid reinforcement, VT swap to downsize cells, etc.
Event Type
Engineering Track Poster
TimeTuesday, June 255:37pm - 5:38pm PDT
LocationLevel 2 Exhibit Hall
Back-End Design
Embedded Systems
Front-End Design
IP