A domain knowledge-informed design space exploration methodology for mechanical layout design

Abstract

Layout designs have a large number of design variables and various physical constraints. Conventional design exploration approaches are time-consuming and may require human intervention. A unique feature about physical layout designs is the availability of domain knowledge, which can be utilised to speed up the design process. In this paper, we propose a generative deep learning-based design space exploration (DSE) methodology that is capable of learning design constraints in the layout design without explicit supervision. Moreover, it can incorporate domain knowledge in the generated layouts, thereby speeding up the design process. This is realised by constructing a layout generation variational autoencoder (LGVAE) model, which uses a latent space as an interface to generate the layouts. By training the LGVAE model, significantly lower-dimensional representations can be learned compared to the original dimensionality of the design space. Therefore, the number of design variables is greatly reduced. We showcase the performance of the proposed DSE approach by solving the heat source layout design problem encountered in thermal management of chips. Experiments demonstrate that the LGVAE model is capable of generating compressed latent representations that capture the characteristics of the input samples, which makes the DSE cost-effective.

KEYWORDS:

• Domain knowledge
• design space exploration
• generative deep learning
• AI argumentation
• heat source layout

Acknowledgements

This research was supported by National Natural Science Foundation of China (No. 52375272) and Zhejiang Provincial Natural Science Foundation of China (No. LR22E050006)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics in publishing

All research conducted in this paper rigorously adheres to ethical standards.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Declaration of generative AI in scientific writing

No AI tool is used in writing this paper.

Submission declaration and verification

The work described has not been published previously and is not under consideration for publication elsewhere.

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 52375272]; Natural Science Foundation of Zhejiang Province: [Grant Number LR22E050006].