Machine Learning-Based Hotspot Detection: Fallacies, Pitfalls and Marching Orders

Reddy, Gaurav Rajavendra; Madkour, Kareem; Makris, Yiorgos

doi:10.1109/iccad45719.2019.8942128

Cited by 20 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, wrong predictions are difficult to explain and also difficult to prevent. This has been demonstrated by Reddy et al [149] as discussed earlier. Second, such models are susceptible to small perturbations in the input.…”

Section: Interpretability and Adversarial Attackssupporting

confidence: 63%

See 1 more Smart Citation

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Rapp

Amrouch

Lin

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in computer-aided design (CAD) that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions.

show abstract

Section: Interpretability and Adversarial Attackssupporting

confidence: 63%

“…This may create a false sense of accuracy. A recent case was presented by Reddy et al [149]. They revisited the ICCAD'12 benchmark that is widely used to train and test lithographic hotspot detection [150].…”

Section: Limited Availability Of Training Datamentioning

confidence: 99%

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Rapp

Amrouch

Lin

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

show abstract

“…To solve this issue, we proposed to increase the dataset diversity by including multiple types of designs and adding more rules variations. The community has studied this issue; its nature makes it challenging to achieve high detection rates to "never seen" data [10]. The work presented in [10] suggests novel augmentation methods to improve the classication rates.…”

Section: Testing New Layouts Processmentioning

confidence: 99%

“…The community has studied this issue; its nature makes it challenging to achieve high detection rates to "never seen" data [10]. The work presented in [10] suggests novel augmentation methods to improve the classication rates. Finally, the time to test a full layout 1000`< x 1000`< equivalent to 5, 000 samples takes less than 13 seconds.…”

Section: Testing New Layouts Processmentioning

confidence: 99%

Design Rule Checking with a CNN Based Feature Extractor

Francisco

Lagare

Jain

et al. 2020

Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD

View full text Add to dashboard Cite

Design rule checking (DRC) is getting increasingly complex in advanced nodes technologies. It would be highly desirable to have a fast interactive DRC engine that could be used during layout. In this work, we establish the proof of feasibility for such an engine. The proposed model consists of a convolutional neural network (CNN) trained to detect DRC violations. The model was trained with articial data that was derived from a set of 50 SRAM designs. The focus in this demonstration was metal 1 rules. Using this solution, we can detect multiple DRC violations 32G faster than Boolean checkers with an accuracy of up to 92%. The proposed solution can be easily expanded to a complete rule set.

show abstract

“…First, inaccurate predictions are challenging to both explain and avoid. As was previously said, Reddy et al [59] have proven this. Second, these models are vulnerable to slight changes in the input.…”

Section: Challenges In ML For Cadmentioning

confidence: 56%

Application of Machine Learning to Physical Design

B¹

View full text Add to dashboard Cite

Machine Learning-Based Hotspot Detection: Fallacies, Pitfalls and Marching Orders

Cited by 20 publications

References 20 publications

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

Design Rule Checking with a CNN Based Feature Extractor

Application of Machine Learning to Physical Design

Contact Info

Product

Resources

About