SHF: Medium: Automating High Level Synthesis via Graph-Centric Deep Learning

SHF：中：通过以图为中心的深度学习实现高级综合自动化

• 批准号: 2211557
• 负责人: Jason Cong
• 金额: $ 120万
• 依托单位: University of California-Los Angeles
• 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211557&HistoricalAwards=false
• 关键词: SHF; Medium; Automating; Level; Synthesis

Domain-specific accelerators (DSAs), such as those developed in recent years to accelerate deep learning applications, have been shown to offer significant performance and energy efficiency over general-purpose CPUs to meet the ever-increasing performance needs. However, DSAs are hard to design and require deep hardware and circuit-design knowledge to achieve high performance, which are lacking by most software programmers. Although the recent advances in high-level synthesis (HLS) tools made it possible to compile high-level software programs to circuit designs, one still needs to have extensive experience to perform microarchitecture optimizations by restructuring or augmenting the programs, which presents a significant barrier to a typical application-domain expert or software developer to design a DSA. The project aims to leverage machine learning and AI techniques to automate microarchitecture optimization and enable a typical software programmer to be able to design highly efficient hardware DSAs, with the quality comparable to those designed by well-trained circuit designers. As a result, it will enable wider and more rapid adoption of customized computing using DSAs to achieve significant improvement in computing efficiency. This project also plans to integrate the research with education to expose students to exciting opportunities in applying AI and ML techniques to electronic design automation, and broaden the participation in computing via high-school summer programs and partnership with the Center for Excellence in Engineering and Diversity (CEED) and Women in Engineering at UCLA.The project addresses two challenges in automating program transformation for HLS microarchitecture optimization: (1) the evaluation of each HLS design is time-consuming; and (2) the HLS design space is extremely large for brute-force search. The project develops a fully automated framework, named DeepAccel, for evaluating and optimizing the microarchitecture of a DSA design without the invocation of the time-consuming HLS tools. It represents the input C/C++ program as one or a set of graphs with the proper data-flow and control-flow information, including auto-inserted optimization directives (pragmas), and then makes use of the latest advances in graph-based machine learning (ML) and ML-driven optimizations to quickly evaluate each solution candidate and guide the optimization process. The approach is transformative, including the following research components: (1) the project tackles the fundamental representation problem on how to represent programs and associated transformations via graph-representation learning so one can apply the latest advances in deep learning, such as graph neural networks, knowledge distillation, meta-learning, and casual inferencing, to HLS design optimization; (2) the project designs trustworthy and adaptive deep-learning models for HLS performance prediction based on biased and sparsely labeled dataset; and (3) the project uses reinforcement learning and other scalable search algorithms to effectively cope with the combinatoric explosion of the search space. Based on these capabilities, DeepAccel is expected to automate the DSA design process for most performance-oriented software programmers.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.

近年来开发的域特异性加速器（DSA）已被证明可以为通用CPU提供出色的性能和能源效率，以满足不断增加的性能需求。但是，DSA很难设计，需要深层的硬件和电路设计知识才能实现高性能，这是大多数软件程序员所缺乏的。尽管最近的高级合成（HLS）工具的最新进展使得可以将高级软件程序编译到电路设计中，但仍然需要具有丰富的经验来通过重组或增强程序来执行微体系结构优化，这为典型的应用程序 - 应用程序 - 销售专家或软件开发人员提供了设计DSA的重要障碍。该项目旨在利用机器学习和AI技术来自动化微体系结构优化，并使典型的软件程序员能够设计高效的硬件DSA，其质量与训练有素的电路设计师设计的质量相当。 结果，它将能够使用DSA来更广泛，更快地采用定制计算，从而在计算效率方面取得显着提高。该项目还计划将研究与教育整合在一起，以使学生获得应用AI和ML技术来进行电子设计自动化的激动人心的机会，并通过高中夏季计划扩大参与计算的参与，并与工程和多样性（CEED）的卓越中心（CEED）和工程中的妇女在UCLA中进行挑战。 HLS设计耗时； （2）HLS设计空间非常大，用于蛮力搜索。该项目开发了一个完全自动化的框架，名为DeepAccel，用于评估和优化DSA设计的微观结构，而无需调用耗时的HLS工具。它代表输入C/C ++程序是具有适当的数据流和控制流信息的一组或一组图形，包括自动插入的优化指令（PRAGMAS），然后利用基于图的机器学习（ML）和ML驱动的优化中的最新进展，以快速评估每个解决方案候选候选者并指导每个解决方案候选过程并指导优化过程。该方法具有变革性，包括以下研究组成部分：（1）该项目解决了如何通过图形代表学习来表示程序和相关转换的基本表示问题，因此人们可以在深度学习中应用最新进展，例如图形神经网络，知识蒸馏，元学习和偶然的地狱，以达到HLS设计的优化； （2）基于偏见且稀疏标记的数据集的HLS性能预测的值得信赖和自适应深度学习模型； （3）该项目使用增强学习和其他可扩展的搜索算法来有效应对搜索空间的组合爆炸。基于这些功能，DeepAccel有望为大多数面向性能的软件程序员自动化DSA设计过程。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来获得支持的。

项目成果

Question-Answer Sentence Graph for Joint Modeling Answer Selection

联合建模答案选择的问答句图

• DOI: 10.18653/v1/2023.eacl-main.68
• 发表时间: 2023
• 期刊: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL
• 作者: Iyer, Roshni;Vu, Thuy;Moschitti, Alessandro;Sun, Yizhou
• 通讯作者: Sun, Yizhou

HOPE: High-order Graph ODE For Modeling Interacting Dynamics

• 发表时间: 2023
• 作者: Xiao Luo;Jingyang Yuan;Zijie Huang;Huiyu Jiang;Yifang Qin;Wei Ju;Ming Zhang;Yizhou Sun

PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction

• DOI: 10.1145/3543507.3583511
• 发表时间: 2023-02
• 期刊: Proceedings of the ACM Web Conference 2023
• 作者: Shichang Zhang;Jiani Zhang;Xiang Song;Soji Adeshina;Da Zheng;C. Faloutsos;Yizhou Sun

Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog

• DOI: 10.18653/v1/2023.acl-industry.18
• 发表时间: 2023
• 作者: Kewei Cheng;Xian Li;Zhengyang Wang;Chenwei Zhang;Binxuan Huang;Y. Xu;X. Dong;Yizhou Sun

CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems

• DOI: 10.1145/3580305.3599272
• 发表时间: 2023-06
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Song Jiang;Zijie Huang;Xiao Luo;Yizhou Sun