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 perspective 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，人工智能驱动方案已成为强大工具，但它们通常是对现有方法的增强而非重构。这类方案常将其他领域（如视觉、文本和图分析）的深度学习模型重新应用于电路设计，而未能针对电子电路的独特复杂性进行适配。这种AI4EDA方法未能实现整体设计综合与理解，忽视了电路数据在电气、逻辑和物理层面的复杂交互。本前瞻性论文主张从AI4EDA向AI原生EDA的范式转变，将人工智能置于设计流程的核心。实现这一愿景的关键在于开发多模态电路表示学习技术，通过协调并提取功能规范、RTL设计、电路网表和物理布局等多源数据的洞见，提供全面理解。我们倡导构建本质多模态的大型电路模型（LCMs），使其能够解码和表达电路数据的丰富语义与结构，从而催生更具韧性、高效性和创造性的设计方法论。秉承AI原生理念，我们预见将超越当前EDA创新瓶颈的发展轨迹，引发电子设计方法的深刻左移。预期进展不仅标志着现有EDA工具的演进，更将是一场革命——催生新型设计工具，有望显著提升设计生产力，开启电路性能、功耗与面积（PPA）优化不再渐进式改进、而是通过跨越式突破重新定义电子系统能力基准的新纪元。