RIVER架构:fpga上并行流处理的可重构流和结构

Dominic Hillenbrand, Christian Brugger, J. Tao, Shufan Yang, M. Balzer
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引用次数: 3

摘要

RIVER架构是一种运行时可配置和可编程的结构,用于fpga上的并行流处理。RIVER的内存架构被设计成能够有效和实时地支持重要的数据流。各个数据处理核心被称为动态流引擎(DSE)。我们的云计算支持的设计流程可以提前生成数十万个不同的DSE内核。在运行时,用户可以根据自己的需求(例如面积和功耗)下载预合成的DSE内核。此外,我们的设计流程使用户免于传统的HDL设计流程和繁琐的设计优化。然而,我们并不阻碍架构变更,而是通过自定义指令和大量设计时选项为它们提供支持。我们的结果表明,我们的架构在计算和内存密集型内核(如二维卷积)中表现良好。与最近发布的高度专业化的体系结构相比,我们实现了更高的时钟速度,并提供了额外的、重要的功能,这要归功于我们复杂的内存体系结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RIVER architecture: Reconfigurable flow and fabric for parallel stream processing on FPGAs
The RIVER architecture is a run-time configurable and programmable fabric for parallel stream processing on FPGAs. RIVER's memory architecture has been designed to support non-trivial data flows efficiently and in real-time. The individual data processing cores are called Dynamic Streaming Engines (DSE). Our cloud computing supported design flow generates hundreds of thousands different DSE cores ahead in time. At run-time users may download pre-synthesized DSE cores according to their requirements - for example area and power consumption. Furthermore, our design flow shields users from traditional HDL design flows and tedious design optimizations. However, we do not impede architectural changes but provide support for them through custom instructions and numerous design time options. Our results suggest that our architecture performs well for computational- and memory-intensive kernels such as 2-dimensional convolution. By comparison to recently published, highly specialized architectures we achieve higher clock speeds and offer additional, non-trivial features thanks to our sophisticated memory architecture.
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