Prevailing data-intensive applications, such as artificial intelligence and internet of things, demand considerable compute capability. Coarse-grained reconfigurable architectures (CGRAs) can meet this demand via providing abundant compute resources. However, compilation has become an essential problem because the increasing resources need to be orchestrated efficiently. Static compilation is insufficient due to conservative resource allocation and exponentially increasing time cost while state-of-the-art dynamic compilation still performs poorly in both generality and efficiency. This article proposes a dynamic compilation system for CGRAs through online pattern-based configuration transformation, which enables virtualization to improve resource utilization and flexibility. It utilizes statically-generated patterns to straightforwardly determine dynamic placement of registers and operations so that the transformation algorithm has a low complexity. Domain-specific features are extracted by a k-means clustering algorithm to help improve the quality of patterns. The experimental results show that statically compiled applications can be transformed onto arbitrary resources at runtime, reserving 73.5 (22.8-163.3 percent) of the original performance$/$/resource on average, 9.1 (0-52.9 percent) better than the state-of-the-art non-general methods.

中文翻译：

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基于模式的CGRA在线配置转换动态编译系统

盛行的数据密集型应用程序，例如人工智能和物联网，需要相当大的计算能力。粗粒度可重构架构 (CGRA) 可以通过提供丰富的计算资源来满足这一需求。然而，编译已成为一个基本问题，因为需要有效地编排不断增加的资源。由于保守的资源分配和成倍增加的时间成本，静态编译是不够的，而最先进的动态编译在通用性和效率方面仍然表现不佳。本文提出了一种通过基于模式的在线配置转换的 CGRA 动态编译系统，使虚拟化能够提高资源利用率和灵活性。它利用静态生成的模式来直接确定寄存器和操作的动态放置，从而使转换算法具有较低的复杂性。特定领域的特征由 k 均值聚类算法提取，以帮助提高模式质量。实验结果表明，静态编译的应用程序可以在运行时转换为任意资源，保留原始性能的 73.5（22.8-163.3%）美元/美元/平均而言，比最先进的非通用方法好 9.1（0-52.9%）。