SHF: Small: Graph-X: Exploiting Hidden Parallelism of Irregular and Non-Stencil Computation in High-Level Synthesis

SHF：小：Graph-X：在高级综合中利用不规则和非模板计算的隐藏并行性

• 批准号: 1908177
• 负责人: Mingjie Lin
• 金额: $ 30.63万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908177&HistoricalAwards=false
• 关键词: SHF; Small; Graph; Exploiting; Hidden

Modern Field Programmable Gate Arrays (FPGAs) hold great promise in leapfrogging our computing performance and energy efficiency in many important AI-driven or big data applications. However, manual FPGA programming is extremely difficult, error-prone, and hard to optimize. This project aims at developing a novel and "automatic" methodology to greatly improve the FPGA developer's productivity and the computing performance of their designs. This project, if successful, can not only achieve the high-performance of FPGA computing previously infeasible, but also significantly boost the FPGA developers' total productivity. Given the growing importance of modern FPGA technologies in our high-tech future, solving this fundamental problem is crucial to the future competitiveness of US computer industry.The major technical aspect that this project seeks to address is how to optimally synthesize irregular yet important computation patterns because all of the most important FPGA designs typically exhibit much more general and sophisticated memory access patterns. To this end, this project has proposed several novel and fundamental graph-based optimization techniques that can not only greatly benefit FPGA technology, but also be readily extended to other computing domains, such as CPU compiler or GPU-based computing. In particular, the project uses graphs to capture memory accesses, and presents three research thrusts: (i) GraphMorph solves the problem of scheduling and binding, (ii) GraphFold maximizes conflict resolution, and (iii) GraphProjection targets high-dimensional memory spaces.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.

现代现场可编程门阵列 (FPGA) 在许多重要的人工智能驱动或大数据应用中有望实现计算性能和能源效率的飞跃。 然而，手动 FPGA 编程极其困难、容易出错且难以优化。 该项目旨在开发一种新颖的“自动”方法，以极大地提高 FPGA 开发人员的工作效率及其设计的计算性能。 该项目如果成功，不仅可以实现以前无法实现的FPGA计算的高性能，还可以显着提升FPGA开发人员的总体生产力。 鉴于现代 FPGA 技术在高科技未来中的重要性日益增加，解决这一基本问题对于美国计算机行业未来的竞争力至关重要。该项目寻求解决的主要技术问题是如何优化地综合不规则但重要的计算模式因为所有最重要的 FPGA 设计通常都表现出更加通用和复杂的内存访问模式。 为此，该项目提出了几种新颖且基础的基于图的优化技术，这些技术不仅可以使 FPGA 技术受益匪浅，而且还可以轻松扩展到其他计算领域，例如 CPU 编译器或基于 GPU 的计算。特别是，该项目使用图来捕获内存访问，并提出了三个研究主旨：（i）GraphMorph 解决调度和绑定问题，（ii）GraphFold 最大化冲突解决，（iii）GraphProjection 针对高维内存空间。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。

项目成果

Constructive Policy: Reinforcement Learning Approach for Connected Multi-Agent Systems

建设性政策：互联多智能体系统的强化学习方法

• DOI: 10.1109/coase.2019.8843223
• 发表时间: 2019-08-01
• 期刊: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
• 作者: Sayyed Jaffar Ali Raza;Mingjie Lin
• 通讯作者: Mingjie Lin

Policy Reuse in Reinforcement Learning for Modular Agents

模块化代理强化学习中的策略重用

• DOI: 10.1109/infoct.2019.8710861
• 发表时间: 2019-03
• 期刊: USA.
• 作者: Raza, Sayyed Jaffar;Lin, Mingjie
• 通讯作者: Lin, Mingjie

Exploiting Irregular Memory Parallelism in Quasi-Stencils through Nonlinear Transformation

通过非线性变换利用准模板中的不规则内存并行性

• DOI: 10.1109/fccm.2019.00039
• 发表时间: 2019-04
• 期刊: Proceedings for the 27th International Symposium On Field-Programmable Custom Computing Machines (FCCM 2019
• 作者: Escobedo, Juan;Lin, Mingjie
• 通讯作者: Lin, Mingjie