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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.

集成电路已经通过广泛的计算设备改变了现代生活的方方面面——从个人电脑到专门的加速器和高性能计算集群。随着现代集成系统设计复杂性的不断提高，传统的电子设计自动化算法不能保证设计过程的收敛性，不能预测输出质量，而且往往满足于较低的性能。考虑到在一个新技术节点上开发一个系统花费了数十亿美元，由于系统没有及时准备好推向市场或失去新技术节点的性能优势而导致的利润损失是无法减轻的。该项目研究了一种全新的电路全局路由方法，这是一个关键的自动化设计步骤，也是设计过程中的主要瓶颈。主要目标是以高度并行的方式使用深度学习模型路由电路，以数量级缩短周转设计时间。更广泛地说，这个项目的结果有望将现有的物理设计范式转变为学习驱动的可预测过程，可以及时利用底层技术的优势，充分发挥其潜力。该奖项由联邦指定的西班牙裔服务机构执行，提供了一个独特的机会，与不同的少数民族人口接触，并创造了电路设计、电子设计自动化和机器学习方面的培训机会。因此，该项目预计将产生强大的经济和社会影响。传统路由器是通过一堆棘手的优化设计来解决全局路由的np困难问题，其特点是收敛问题和不可预测的路由质量。虽然人们普遍认为用机器学习（ML）模型实现路由的潜在好处，但还没有一个单一的端到端学习框架被证明可以路由看不见的高分辨率实用集成电路。为了应对这一挑战，将全局路由作为一个ML问题进行研究，其中网络被视为路由解决方案的缺失部分，并在考虑总体最小无线目标和拥塞约束的同时，使用成像ML模型按首选顺序重建。本研究的见解将被用于开发一个强化学习框架，该框架包括：(i)用于编码路由属性的图神经网络，（ii）用于确定下一个要路由的网络的网络排序策略，以及（iii）用于路由单个看不见的网络的变分自编码器。最终的设计方法和机器学习模型、架构和算法将集成到端到端机器学习路由器中，并在工业合作者提供的现有基准和商业产品上进行演示。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。