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.

集成电路通过各种计算设备改变了现代生活的各个领域——从个人计算机到专用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无法保证。设计过程的融合，无法预测输出质量，并且通常会满足于较低的性能，考虑到在新技术节点上开发系统花费了数十亿美元，由于系统没有按时发布而造成利润损失。市场或失去性能优势该项目研究了一种全新的电路全局布线方法——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。更广泛地说，该项目的结果有望将现有的物理设计范例转向学习驱动的可预测过程，从而充分利用底层技术的优势。及时执行。作为联邦政府指定的西班牙裔服务机构，该奖项提供了与多元化少数群体接触的独特机会，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，该项目预计将具有强大的经济和社会效益。传统路由器通过一系列棘手的优化来解决全局路由的 NP 难题，其特点是收敛问题和不可预测的路由质量，但人们普遍认为通过机器学习 (ML) 实现路由的潜在好处。）模型，不是单一的端到端学习框架已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，全局路由将作为机器学习问题进行研究，其中网络被视为路由解决方案的缺失部分并进行重建，按照首选顺序，使用成像 ML 模型，同时考虑总体最小线长目标和拥塞约束，将利用本研究的见解来开发强化学习框架，包括：（i）用于编码路由属性的图神经网络，（ii） ) ) 净订购政策用于确定下一个要路由的网络，以及（iii）用于路由各个看不见的网络的变分自动编码器，最终的设计方法和机器学习模型、架构将集成到在现有基准和商业产品上演示的端到端机器学习路由器和算法中。由工业合作者提供。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。