Collaborative Research: SHF: Medium: Differentiable Hardware Synthesis

合作研究：SHF：媒介：可微分硬件合成

• 批准号: 2403135
• 负责人: Zhiru Zhang
• 金额: $ 45万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2403135&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Differentiable

In the rapidly evolving digital world, creating high-performance and efficient computer hardware is crucial. Electronic design automation (EDA), a process that automates and optimizes the design of hardware, becomes even more critical and challenging with the ever-increasing complexity increases. This project introduces a novel approach to EDA, by solving circuit optimization problems with a blend of formal methods, machine learning, and parallel computing. This proposed research aims to transform the way computer chips are made, making the design process faster, less expensive, and more adaptable. The research findings and tools will be made publicly available to facilitate technology transfers and industry-academia interactions in a multidisciplinary community. The research findings and tools will be made publicly available to support technology transfers and interactions between industry and academia in a multidisciplinary community. This effort will also include active participation in educational and workforce development initiatives, involving high-school students and students from underrepresented groups.In addressing the inherent limitations of existing synthesis solutions, such as unfavorable speed-quality trade-offs and inflexibility in leveraging domain knowledge, the presented research introduces a novel strategy that combines formal techniques with learning-based optimization. Specifically, the research takes a radically different approach by creating differentiable hardware synthesis techniques that are well-suited for heterogeneous computing. The key strategy involves the combination of formal techniques with learning-based optimization, which facilitates efficient global optimization, with or without the need for training data, while taking advantage of the computational power of parallel computing devices like graphics processing units (GPUs). This new approach distinguishes itself from conventional methods by its ability to scale global optimization through parallel computing resources, as well as its potential to combine other machine learning models to enable data-driven optimization via back-propagation. The developed algorithms and software will be made open-source and publicly accessible with comprehensive tutorials and educational materials.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.

在快速发展的数字世界中，创建高性能和高效的计算机硬件至关重要。电子设计自动化（EDA）是自动化和优化硬件设计的过程，随着不断增长的复杂性而变得更加至关重要和具有挑战性。该项目通过将正式方法，机器学习和并行计算的混合物解决电路优化问题介绍了一种新颖的EDA方法。这项拟议的研究旨在改变制造计算机芯片的方式，从而使设计过程更快，更便宜且更适合。研究发现和工具将公开使用，以促进多学科社区中的技术转移和行业 - academia互动。研究发现和工具将公开使用，以支持多学科社区中行业和学术界之间的技术转移和互动。这项工作还将包括积极参与教育和劳动力发展计划，涉及高中生的学生和来自代表性不足的小组的学生。在解决现有合成解决方案的固有局限性，例如不利的速度质量折衷以及在利用领域知识中的不利速度质量折衷和不利于的稳健性，介绍的研究介绍了一种与学习基础技术相结合的新型策略。具体而言，该研究通过创建适合异质计算的可区分硬件合成技术来采用根本不同的方法。关键策略涉及形式技术与基于学习的优化的组合，这有助于有效的全球优化，无论是否需要培训数据，同时利用了平行计算设备（例如图形处理单元（GPU））的计算能力。这种新方法通过其通过并行计算资源扩展全局优化的能力以及将其他机器学习模型相结合以通过反向传播来启用数据驱动优化的潜力来区别于常规方法。已开发的算法和软件将通过全面的教程和教育材料进行开源，并可以公开访问。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。