SHF: SMALL: End-to-End Global Routing with Reinforcement Learning in VLSI Systems

SHF：小型：VLSI 系统中采用强化学习的端到端全局路由

基本信息

批准号：
2151854
负责人：
Inna Partin-Vaisband
金额：
$ 49.96万
依托单位：
University of Illinois at Chicago
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151854&HistoricalAwards=false
关键词：
SHF SMALL End Global Routing

项目摘要

Integrated circuits have transformed every sector of modern life with a broad range of computing devices – from personal computers to specialized accelerators and high-performance computing clusters. With the ever-high design complexity of modern integrated systems, traditional electronic design-automation algorithms cannot guarantee convergence of the design process, fail to predict output quality, and often settle for lower performance. Considering billions of dollars spent on developing a system in a new technology node, the loss of profit due to not having the system ready on time for release to market or losing the performance benefits of the new technology node cannot be mitigated. This project investigates a fundamentally new approach for circuit global routing -- a critical automated design step and a primary bottleneck in the design process. The primary objective is to route circuits with deep-learning models in a highly parallelizable manner, shortening the turnaround design time by orders of magnitude. More broadly, the results from this project are expected to shift existing physical-design paradigms toward a learning-driven predictable process that can exploit the advantages of the underlying technology to their full potential in a timely manner. Executed by a federally designated Hispanic Serving Institution, this award presents a unique opportunity to engage with a diverse minority population and creates training opportunities in circuit design, electronic design automation, and machine learning. As such, the project is anticipated to have a strong economic and societal impact.Designed via a pile of intractable optimizations to tackle the NP-hard problem of global routing, traditional routers are characterized by convergence issues and unpredictable routing quality. While there is a general agreement on potential benefits of realizing routing with machine-learning (ML) models, not a single end-to-end learning framework has been demonstrated to route unseen high-resolution practical integrated circuits.To address this challenge, global routing will be investigated as an ML problem in which nets are viewed as the missing parts of a routing solution and reconstructed, in a preferred order, with imaging ML models while considering the overall minimum wirelength objective and congestion constraints. The insights from this study will be exploited to develop a reinforcement-learning framework comprising: (i) graph neural network for encoding routing attributes, (ii) net ordering policy for determining the next net to be routed, and (iii) variational autoencoder to route individual unseen nets. The resulting design methodology and ML models, architectures, and algorithms will be integrated in an end-to-end ML router and demonstrated on existing benchmarks and commercial products provided by industrial collaborators.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.

综合电路已经通过广泛的计算设备（从个人计算机到专业加速器和高性能计算簇）改变了现代生活的每个部门。随着现代集成系统的最高设计复杂性，传统的电子设计自动化算法无法保证设计过程的融合，无法预测输出质量，并且通常会降低性能。考虑到在新技术节点中开发系统上数十亿美元的支出，由于无法按时发布该系统的损失，因此无法按时发布该系统或失去新技术节点的性能收益。该项目研究了一种从根本上进行全新的全局路由方法，这是一个关键的自动化设计步骤和设计过程中的主要瓶颈。主要目的是以高度可行的方式路由带有深入学习模型的电路，从而通过数量级缩短周转时间。更广泛地说，该项目的结果有望将现有的物理设计范式转移到以学习为导向的可预测过程中，该过程可以及时探索基础技术的优势及其全部潜力。该奖项由联邦指定的西班牙裔服务机构执行，为与少数群体的互动提供了独特的机会，并在巡回设计，电子设计自动化和机器学习方面创造了培训机会。因此，预计该项目将产生强烈的经济和社会影响。通过一堆棘手的优化设计来解决全球路由的NP坚硬问题，传统路由器的特征是融合问题和不可预测的路由质量。 While there is a general agreement on potential benefits of realizing routing with machine-learning (ML) models, not a single end-to-end learning framework has been demonstrated to route unseen High-resolution practical integrated circuits.To address this challenge, global routing will be investigated as an ML problem in which nets are viewed as the missing parts of a routing solution and reconstructed, in a preferred order, with imaging ML models while considering the overall minimum wireless objective and拥堵限制。将探讨本研究的见解，以开发一个包括：（i）用于编码路由属性的图形神经网络，（ii）用于确定要路由的下一个网络的网络排序策略，以及（iii）变异自动编码器以路由单个未看到的网络。由此产生的设计方法论和ML模型，体系结构和算法将集成到端到端的ML路由器中，并在工业合作者提供的现有基准和商业产品上进行了证明。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识优点和广泛影响的评估来评估的值得支持的，并被认为是值得的。