Human in the Loop AI EDA for Chip Design

Lei Zhang

doi:10.70393/6a6374616d.333732

Authors

Lei Zhang Clark University

DOI:

https://doi.org/10.70393/6a6374616d.333732

ARK:

https://n2t.net/ark:/40704/JCTAM.v3n1a05

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

28

Keywords:

Human in The Loop Learning, Electronic Design Automation, Chip Placement and Routing, Preference Feedback, Cost–benefit Analysis

Abstract

Currently, chip design automation is hampered by factors such as data scarcity, difficulty in sharing, and high costs associated with design rules and verification. Based on the research trajectory of human-machine collaborative learning, this paper proposes a modular framework that embeds expert feedback into three stages: data processing, model training, and system-level intervention, implementing it within the placement and routing workflow. With less human input, the number of iterations decreases. Economic accounting connects marginal quality gains with human and computing costs, exhibiting diminishing returns and context-dependent optimal feedback budgets.

Author Biography

Lei Zhang, Clark University

Clark University, USA.

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