SHF: Small: Explainable Machine Learning for Better Design of Very Large Scale Integrated Circuits

随着芯片技术的进步，集成电路的计算机辅助设计（IC-CAD）变得更加复杂、具有挑战性和耗时。近年来，人工智能（AI）应用于芯片设计和制造的不同阶段的趋势日益明显。事实证明，人工智能的引入通过提供早期反馈来预测设计周期内的潜在故障，使芯片设计过程更加高效和可靠。该项目的目标是利用新兴的可解释人工智能（XAI）领域来改变芯片设计过程中AI的效用。该项目通过提供重要的可解释性维度与AI辅助芯片设计直接相关。可解释性提供了对设计失败原因和根本原因的理解，还允许设计师确定最佳策略，以避免在设计周期后期发生失败。通过提供有关人工智能预测失败的有效和早期反馈，增强人工智能预测的可信度，并为设计师与IC-CAD工具合作开发一种新的范式，XAI的解释有可能改变传统的芯片设计过程。该项目的研究结果将立即引起IC-CAD和芯片设计公司的兴趣，因为他们承诺缩短电子产品的上市时间，并与2022年半导体芯片法案保持一致。该项目将为优秀的本科生提供研究机会，主要针对妇女和少数民族。研究人员将向国内半导体和IC-CAD公司展示该项目的研究成果，以提供实习，就业和合作的机会，这直接有助于劳动力发展并提高美国在全球半导体市场的竞争力。该项目的任务涵盖了芯片设计流程中的各种情况，包括：（1）研究如何使用XAI在设计周期的早期避免芯片布局违反设计规则;（2）研究XAI如何更好地指导与硬件中机器学习应用合成相关的优化;（3）研究XAI对芯片布局混淆AI的好处-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

With the advance of chip technology, Computer-Aided Design of Integrated Circuits (IC-CAD) becomes more complex, challenging, and time-consuming. Recent years have seen a rising trend of artificial intelligence (AI) applied to different stages of chip design and manufacturing. The incorporation of AI has been shown to make the chip design process more efficient and reliable by providing early feedback to predict potential failures within the design cycle. The goal of this project is to utilize the emerging field of Explainable Artificial Intelligence (XAI) to transform the utility of AI within the chip design process. This project is immediately relevant to AI-assisted chip design by providing an important explainability dimension. Explainability provides an understanding of why a design is predicted to be failing and root-causing it. It also allows the designer to identify the best strategy to avoid the failure from occurring later in the design cycle. Explanations made with XAI have the potential to transform the traditional chip design process by providing effective and early feedback about AI-predicted failures, enhancing trustworthiness to AI predictions, and overall developing a novel paradigm for designers to collaborate with the IC-CAD tools. Research findings from this project will be of immediate interest to IC-CAD and chip design companies because of their promise to reduce time-to-market of electronic products and align well with the 2022 Semiconductor CHIPS Act. The project will provide research opportunities for top undergraduate students, primarily targeting women and minorities. The investigator will present research findings of this project to domestic semiconductor and IC-CAD companies to provide opportunities for internship, employment, and collaboration which directly contribute to workforce development and enhance competitiveness of the United States in the global semiconductor market. The tasks in the project cover a wide range of cases within the chip design flow including: (1) investigating how XAI can be used to avoid design rule violations on the chip layout early-on in the design cycle; (2) investigating how XAI can better guide optimizations related to synthesis of machine learning applications in hardware; (3) investigating the benefits of XAI for chip layout obfuscation against AI-based security attacks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.