Conference proceeding
Graph Representation Learning for Gate Arrival Time Prediction
MLCAD '22: PROCEEDINGS OF THE 2022 ACM/IEEE 4TH WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD), pp 127-133
01 Jan 2022
Abstract
An accurate estimate of the timing profile at different stages of the physical design flow allows for pre-emptive changes to the circuit, significantly reducing the design time and effort. In this work, a graph based deep regression model is utilized to predict the gate level arrival time of the timing paths of a circuit. Three scenarios for post routing prediction are considered: prediction after completing floorplanning, prediction after completing placement, and prediction after completing clock tree synthesis (CTS). A commercial static timing analysis (STA) tool is utilized to determine the mean absolute percentage error (MAPE) and the mean absolute error (MAE) for each scenario. Results obtained across all models trained on the complete dataset indicate that the proposed methodology outperforms the baseline errors produced by the commercial physical design tools with an average improvement of 61.58% in the MAPE score when predicting the post-routing arrival time after completing floorplanning and 13.53% improvement when predicting the post-routing arrival time after completing placement. Additional prediction scenarios are analyzed, where the complete dataset is further sub-divided based on the size of the circuits, which leads to an average improvement of 34.83% in the MAPE score as compared to the commercial tool for post-floorplanning prediction of the post-routing arrival time and 22.71% improvement for post-placement prediction of the post-routing arrival time.
Metrics
Details
- Title
- Graph Representation Learning for Gate Arrival Time Prediction
- Creators
- Pratik Shrestha - Drexel UniversitySaran Phatharodom - Drexel UniversityIoannis Savidis - Drexel UniversityAssoc Comp Machinery
- Publication Details
- MLCAD '22: PROCEEDINGS OF THE 2022 ACM/IEEE 4TH WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD), pp 127-133
- Conference
- MLCAD '22: 2022 ACM/IEEE 4TH WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD), 4th
- Publisher
- Assoc Computing Machinery
- Number of pages
- 7
- Grant note
- CNS-1751032 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; College of Engineering
- Web of Science ID
- WOS:000866282100021
- Scopus ID
- 2-s2.0-85139178552
- Other Identifier
- 991019240283304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Engineering, Manufacturing