Rosetta:软件可编程fpga的现实高级综合基准套件

Yuan Zhou, Udit Gupta, Steve Dai, Ritchie Zhao, Nitish Kumar Srivastava, Hanchen Jin, Joseph Featherston, Yi-Hsiang Lai, Gai Liu, Gustavo Angarita Velasquez, Wenping Wang, Zhiru Zhang
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引用次数: 89

摘要

现代高级综合(HLS)工具大大减少了设计和实现复杂的基于fpga的加速器的周转时间。它们还暴露了各种优化机会,这些机会在寄存器传输级别上不容易探索。随着HLS设计方法的越来越多的采用和合成优化的不断进步,人们越来越需要现实的基准来(1)促进工具之间的比较,(2)评估和压力测试新的合成技术,以及(3)建立有意义的性能基线来跟踪HLS技术的进展。虽然已经存在几个HLS基准套件,但它们主要由小型教科书样式的函数内核组成,而不是完整而复杂的应用程序。为了解决这一限制,我们介绍了Rosetta,一个现实的软件可编程fpga基准套件。Rosetta中的设计是完全开发的应用程序。它们与实际的性能约束相关联,并使用现代HLS工具的高级功能进行了优化。我们相信,Rosetta不仅对HLS研究社区有用,而且还可以作为非HLS专家用户的一套设计教程。在本文中,我们描述了我们的基准测试的特点和应用于它们的优化技术。我们进一步报告了在嵌入式FPGA器件和云FPGA平台上的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rosetta: A Realistic High-Level Synthesis Benchmark Suite for Software Programmable FPGAs
Modern high-level synthesis (HLS) tools greatly reduce the turn-around time of designing and implementing complex FPGA-based accelerators. They also expose various optimization opportunities, which cannot be easily explored at the register-transfer level. With the increasing adoption of the HLS design methodology and continued advances of synthesis optimization, there is a growing need for realistic benchmarks to (1) facilitate comparisons between tools, (2) evaluate and stress-test new synthesis techniques, and (3) establish meaningful performance baselines to track progress of the HLS technology. While several HLS benchmark suites already exist, they are primarily comprised of small textbook-style function kernels, instead of complete and complex applications. To address this limitation, we introduce Rosetta, a realistic benchmark suite for software programmable FPGAs. Designs in Rosetta are fully-developed applications. They are associated with realistic performance constraints, and optimized with advanced features of modern HLS tools. We believe that Rosetta is not only useful for the HLS research community, but can also serve as a set of design tutorials for non-expert HLS users. In this paper we describe the characteristics of our benchmarks and the optimization techniques applied to them. We further report experimental results on an embedded FPGA device as well as a cloud FPGA platform.
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