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.

现代现场可编程栅极阵列（FPGA）在跨越我们在许多重要的AI驱动或大数据应用程序中超越我们的计算性能和能源效率方面拥有巨大的希望。 但是，手动FPGA编程非常困难，容易出错，并且难以优化。 该项目旨在开发一种新颖的“自动”方法，以极大地提高FPGA开发人员的生产率和设计的计算性能。 如果成功的话，这个项目不仅可以实现以前不可行的FPGA计算的高性能，而且还可以显着提高FPGA开发人员的总生产率。 鉴于现代FPGA技术在我们的高科技未来中的重要性越来越重要，因此解决这个基本问题对于美国计算机行业的未来竞争力至关重要。该项目试图解决的主要技术方面是如何最佳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大的一般性和复杂的记忆访问和复杂的记忆访问。 为此，该项目提出了几种基于图形的新颖和基本的优化技术，这些技术不仅可以极大地使FPGA技术受益，而且很容易将其扩展到其他计算域，例如CPU编译器或基于GPU的计算。特别是，该项目使用图表来捕获内存访问，并提出了三个研究推力：（i）GraphMorph解决了调度和约束力的问题，（ii）GraphFold最大化冲突解决方案，（iii）GraphProvention Attargets高度记忆空间。这些奖项通过NSF的法定宣传和宽广的影响，这是NSF的法规及其范围的范围。 标准。

项目成果

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
• 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
• 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

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

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