Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an AI4EDA approach falls short of achieving a holistic design synthesis and understanding, overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This paper argues for a paradigm shift from AI4EDA towards AI-native EDA, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, RTL designs, circuit netlists, and physical layouts. We champion the creation of large circuit models (LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-native philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound shift-left in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area (PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

翻译：在电子设计自动化（EDA）领域，AI驱动方案已成为强大工具，但这些方案通常是对现有方法的增强而非重新定义。这些方案常借用其他领域（如视觉、文本和图分析）的深度学习模型，将其应用于电路设计而未针对电子电路的独特复杂性进行定制。这种AI4EDA方法未能实现整体设计综合与理解，忽视了电路数据中电气、逻辑和物理层面错综复杂的相互作用。本文主张从AI4EDA向AI原生EDA的范式转变，将AI整合为设计流程的核心。该愿景的关键在于开发多模态电路表征学习技术，通过协调和提取功能规范、RTL设计、电路网表和物理布局等多样化数据源的洞见，提供全面的理解。我们倡导构建天生具有多模态特性的大电路模型（LCM），这些模型旨在解码和表达电路数据的丰富语义与结构，从而培育更具弹性、高效和创新性的设计方法。秉持这一AI原生理念，我们预见一条超越当前EDA创新平台期的轨迹，引发电子设计方法学的深刻左移。所展望的进步不仅预示着现有EDA工具的演进，更是一场革命，催生新型设计工具，有望彻底提升设计效率，开创一个电路性能、功耗与面积（PPA）优化不再增量推进、而是通过重新定义电子系统能力基准的跨越式飞跃的新纪元。