高阶综合中管道不规则回路的动态危险识别

Steve Dai, Ritchie Zhao, Gai Liu, S. Srinath, Udit Gupta, C. Batten, Zhiru Zhang
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引用次数: 30

摘要

当前的高级综合(high-level synthesis, HLS)中的流水线方法对于具有常规和静态可分析内存访问模式的应用程序实现了高性能。但是,它不能有效地处理不常见的与数据相关的结构和数据危险,因为它们被保守地认为总是发生在合成管道中。为了实现不规则循环的高通量流水线,我们研究了使用特定应用的动态危险分辨率来增强HLS的问题,并研究了其对调度和结果质量的影响。我们建议在编译时生成一个积极的管道,同时在运行时通过内存端口仲裁和压缩重放来解决风险。我们针对Xilinx FPGA的实验在一系列代表性基准测试中证明了有希望的性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Hazard Resolution for Pipelining Irregular Loops in High-Level Synthesis
Current pipelining approach in high-level synthesis (HLS) achieves high performance for applications with regular and statically analyzable memory access patterns. However, it cannot effectively handle infrequent data-dependent structural and data hazards because they are conservatively assumed to always occur in the synthesized pipeline. To enable high-throughput pipelining of irregular loops, we study the problem of augmenting HLS with application-specific dynamic hazard resolution, and examine its implications on scheduling and quality of results. We propose to generate an aggressive pipeline at compile-time while resolving hazards with memory port arbitration and squash-and-replay at run-time. Our experiments targeting a Xilinx FPGA demonstrate promising performance improvement across a suite of representative benchmarks.
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