Shift-Left Techniques in Electronic Design Automation: A Survey
Year: 2026
Type: article
Abstract: The chip design process involves numerous steps, beginning with defining product requirements and progressing through architectural planning, system-level design, and the physical layout of individual circuit blocks. As the enablers of large-scale chip development, Electronic Design Automation (EDA) tools play a vital role in helping designers achieve high-quality results. The Shift-Left methodology introduces a pathway toward creating digital twins and fusing multiple design steps, thereby transitioning traditionally sequential, physically-aware processes into virtual design environments. As late-stage fixes are often prohibitively expensive, this shift allows designers to establish stronger correlations earlier and optimize designs more effectively. However, challenges remain, especially in accurately replicating downstream behaviors and determining the right scope and timing for adoption. These challenges, in turn, have revealed new opportunities for EDA vendors, physical designers, and logic designers alike. The rise of AI techniques and the momentum of open-source design flows have also significantly strengthened prediction and modeling capabilities, making data-driven methods increasingly relevant to the EDA community. This, in turn, enhances the “Shift-Left” features embedded in current tools by enabling AI-assisted prediction of downstream design metrics earlier in the flow. As the industry advances toward intelligent EDA tools and techniques, it is timely to reflect on Shift-Left progress made and the challenges that remain. In this article, we present a comprehensive survey of existing and emerging paradigms in Shift-Left research within EDA and the broader design ecosystem. Our goal is to provide a unique perspective on the state of the field and its future directions. Relevant articles mentioned are organized in https://github.com/iCAS-SJTU/Shift-Left-EDA-Papers.
Source: ACM Computing Surveys
DOI: 10.1145/3819588
Authors: Xinyue Wu, Zixuan Li, Fan Hu, Ting Lin, Runxi Wang, Xiaotian Zhao, Xinfei Guo
Institution: Shanghai Jiao Tong University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Award: G3261762312
OpenAlex ID: W4417081432
