MLCAD: A survey of research in machine learning for CAD keynote paper
Due to the increasing size of integrated circuits (ICs), their design and optimization phases
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …
Advancing placement
AB Kahng - Proceedings of the 2021 International Symposium on …, 2021 - dl.acm.org
Placement is central to IC physical design: it determines spatial embedding, and hence
parasitics and performance. From coarse-to fine-grain, placement is conjointly optimized …
parasitics and performance. From coarse-to fine-grain, placement is conjointly optimized …
Machine learning for CAD/EDA: the road ahead
AB Kahng - IEEE Design & Test, 2022 - ieeexplore.ieee.org
Machine Learning for CAD/EDA: The Road Ahead Page 1 1 Machine Learning for CAD/EDA:
The Road Ahead Andrew B. Kahng UCSD CSE and ECE Departments, La Jolla, CA 92093-0404 …
The Road Ahead Andrew B. Kahng UCSD CSE and ECE Departments, La Jolla, CA 92093-0404 …
Metrics2. 1 and flow tuning in the ieee ceda robust design flow and openroad iccad special session paper
In today's RTL-to-GDS flow domain, there is a lack of standards for reporting of design and
tool metrics. Moreover, each tool or engine has its own set of parameters that can change …
tool metrics. Moreover, each tool or engine has its own set of parameters that can change …
RTL-MP: toward practical, human-quality chip planning and macro placement
In a typical RTL-to-GDSII flow, floorplanning plays an essential role in achieving decent
quality of results (QoR). A good floorplan typically requires interaction between the frontend …
quality of results (QoR). A good floorplan typically requires interaction between the frontend …
Routability prediction and optimization using explainable AI
Machine learning (ML) techniques have been widely studied to predict routability in early-
stage. To reduce the design turn-around time during the placement and routing iterations, it …
stage. To reduce the design turn-around time during the placement and routing iterations, it …
A machine learning approach to improving timing consistency between global route and detailed route
VA Chhabria, W Jiang, AB Kahng… - ACM Transactions on …, 2023 - dl.acm.org
Due to the unavailability of routing information in design stages prior to detailed routing
(DR), the tasks of timing prediction and optimization pose major challenges. Inaccurate …
(DR), the tasks of timing prediction and optimization pose major challenges. Inaccurate …
Solvers, Engines, Tools and Flows: The Next Wave for AI/ML in Physical Design
AB Kahng - Proceedings of the 2024 International Symposium on …, 2024 - dl.acm.org
It has been six years since an ISPD-2018 invited talk on" Machine Learning Applications in
Physical Design". Since then, despite considerable activity across both academia and …
Physical Design". Since then, despite considerable activity across both academia and …
ML for Design QoR Prediction
Abstract Design quality of results (QoR) spans metrics of design process outcomes, such as
power, performance, area, or runtime, at all stages of the design process. Prediction of …
power, performance, area, or runtime, at all stages of the design process. Prediction of …
[PDF][PDF] Application of Machine Learning to Physical Design
VB Pawar - San Francisco State University, 2022 - scholarworks.calstate.edu
The special features we may put in SoCs are no longer primarily constrained by chip space
limitations because of Moore's law's exponential increase in transistor density. Instead, the …
limitations because of Moore's law's exponential increase in transistor density. Instead, the …