Collaborative Research: SHF: Medium: Differentiable Hardware Synthesis

在快速发展的数字世界中，创造高性能和高效率的计算机硬件至关重要。电子设计自动化(EDA)是一个自动化和优化硬件设计的过程，随着复杂性的不断增加，它变得更加关键和具有挑战性。这个项目引入了一种新的EDA方法，通过结合形式化方法、机器学习和并行计算来解决电路优化问题。这项拟议的研究旨在改变计算机芯片的制造方式，使设计过程更快、成本更低、适应性更强。研究成果和工具将公之于众，以促进多学科社区的技术转让和产业界与学术界的互动。研究结果和工具将公之于众，以支持多学科社区中产业界和学术界之间的技术转让和互动。这一努力还将包括积极参与教育和劳动力发展倡议，让高中生和来自代表性不足群体的学生参与。为了解决现有综合解决方案的内在局限性，如不利的速度-质量权衡和利用领域知识的缺乏灵活性，本研究引入了一种新的策略，将形式技术与基于学习的优化相结合。具体地说，这项研究采取了一种截然不同的方法，创造了非常适合于异类计算的可区分硬件合成技术。关键策略包括将正式技术与基于学习的优化相结合，这有助于高效的全局优化，无论是否需要训练数据，同时利用图形处理单元(GPU)等并行计算设备的计算能力。这种新方法与传统方法的不同之处在于，它能够通过并行计算资源来扩展全局优化，以及它有可能结合其他机器学习模型，通过反向传播实现数据驱动的优化。开发的算法和软件将开放源代码，公开提供全面的教程和教育材料。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.