Collaborative Research: SHF: Medium: Revitalizing EDA from a Machine Learning Perspective

合作研究：SHF：媒介：从机器学习的角度振兴 EDA

• 批准号: 2106725
• 负责人: Jiang Hu
• 金额: $ 79万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106725&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Revitalizing

Despite its spectacular success in the past, design automation of electronic circuits and systems remains limited in effectiveness and efficiency. This is often due to unnecessarily excessive iterations of point software tools, where early predictions on downstream design steps are overly pessimistic and interoperations among different tools largely require manual handling. As such, existing chip-design flows are not considered fully automated, and there still exists a strong need for jointly exploring the considerable room between the different steps in these flows. Moreover, existing design-verification approaches usually involve unwanted redundancy and substantial manual effort, contributing greatly to a well-known bottleneck of time-to-market. The recent progress in machine-learning technology offers a great opportunity to revitalize current Electronic Design Automation (EDA) flows from an alternative perspective, i.e., extracting design and verification knowledge from existing design data, and reusing it on new designs. The goal of this research is to develop such knowledge extraction and reuse techniques with the aid of the state-of-the-art machine learning technology. The outcome of this research is to help mitigate the chip-design productivity crisis and cater to the increasing demand for hardware-accelerated computing. This research is also training students, including women and under-represented minorities, with interdisciplinary skills and preparing tomorrow’s high-tech workforce in the U.S. for solving challenges in the electronic industry.The project involves systematic research on machine learning in the context of electronic design automation with five integrated components: 1) development of learning-based fast and high fidelity prediction techniques for knowledge extraction in the structural and behavioral domains of circuit designs; 2) a study on how to seamlessly integrate the design predictions with circuit optimizations; 3) applying machine-learning prediction to accelerating functional-verification coverage and facilitating automated debugging; 4) developing autonomous learning on the interplay amongst tools and thereby achieving automated synthesis space exploration; 5) automated machine-learning architecture search and feature refinement in EDA applications.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.

尽管过去取得了惊人的成功，但电子电路和系统的设计自动化在有效性和效率方面仍然有限。这通常是由于对点软件工具的不必要的过多迭代，因此，下游设计步骤的早期预测过于悲观，并且在不同工具之间的互操作很大程度上需要手动处理。因此，现有的芯片设计流不被认为是完全自动化的，并且仍然有很大的需求，可以共同探索这些流中不同步骤之间的相当大的空间。此外，现有的设计验证方法通常涉及不必要的冗余和实质性的手动努力，这对众所周知的上市时间瓶颈做出了巨大的贡献。机器学习技术的最新进展为振兴当前电子设计自动化（EDA）流动提供了一个很好的机会，即从现有设计数据中提取设计和验证知识，并将其重复使用新设计。这项研究的目的是借助最先进的机器学习技术来开发这种知识提取和重用技术。这项研究的结果是帮助减轻芯片设计的生产力危机，并满足对硬件加速计算的需求不断增长的需求。 This research is also training students, including women and under-represented minorities, with interdisciplinary skills and preparing tomorrow’s high-tech workforce in the U.S. for solving challenges in the electronic industry.The project involves systematic research on machine learning in the context of electronic design automation with five integrated components: 1) development of learning-based fast and high fidelity prediction techniques for knowledge extraction in the structural and behavioral domains of circuit designs; 2）关于如何无缝将设计预测与电路优化整合的研究； 3）应用机器学习预测以加速功能验证覆盖范围并支持自动调试； 4）在工具之间的相互作用上开发自主学习，从而实现自动合成空间探索； 5）在EDA应用程序中自动化的机器学习体系结构搜索和功能改进。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被视为通过评估而被视为珍贵的支持。

项目成果

Machine-Learning Based Delay Prediction for FPGA Technology Mapping

• DOI: 10.1145/3557988.3569713
• 发表时间: 2022-11
• 期刊: Proceedings of the 24th ACM/IEEE Workshop on System Level Interconnect Pathfinding
• 作者: Hailiang Hu;Jiang Hu;Fan Zhang;Binghe Tian;Ismail Bustany

A Stochastic Approach to Handle Non-Determinism in Deep Learning-Based Design Rule Violation Predictions

处理基于深度学习的设计规则违规预测中的非确定性的随机方法

• 发表时间: 2022
• 期刊: IEEE/ACM International Conference on Computer-Aided Design
• 作者: Liang, R.;Xiang, H.;Jung, J.;Hu, J.;Nam, G.-J.
• 通讯作者: Nam, G.-J.

FlowTuner: A Multi-Stage EDA Flow Tuner Exploiting Parameter Knowledge Transfer

FlowTuner：利用参数知识传输的多级 EDA 流调谐器

• DOI: 10.1109/iccad51958.2021.9643564
• 发表时间: 2021
• 期刊: IEEE/ACM International Conference on Computer-Aided Design
• 作者: Liang, R.;Jung, J.;Xiang, H.;Reddy, L.;Lvov, A.;Hu, J.;Nam, G.-J.
• 通讯作者: Nam, G.-J.

APOLLO: An Automated Power Modeling Framework for Runtime Power Introspection in High-Volume Commercial Microprocessors

• DOI: 10.1145/3466752.3480064
• 发表时间: 2021-10
• 期刊: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Zhiyao Xie;Xiaoqing Xu;Matt Walker;Joshua Knebel;K. Palaniswamy;Nicolas Hebert;Jiang Hu;Huanrui Yang;Yiran Chen;Shidhartha Das

Towards collaborative intelligence: routability estimation based on decentralized private data

• DOI: 10.1145/3489517.3530578
• 发表时间: 2022-03
• 期刊: Proceedings of the 59th ACM/IEEE Design Automation Conference
• 作者: Jingyu Pan;Chen-Chia Chang;Zhiyao Xie;Ang Li;Minxue Tang;Tunhou Zhang;Jiangkun Hu;Yiran Chen