LASER: A hardware/software approach to accelerate complicated loops on CGRAs
LASER: A hardware/software approach to accelerate complicated loops on CGRAs
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DOI：
10.23919/date.2018.8342170
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发表时间：
2018-04
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通讯作者：
M. Balasubramanian;Shail Dave;Aviral Shrivastava;Reiley Jeyapaul
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作者：
M. Balasubramanian;Shail Dave;Aviral Shrivastava;Reiley Jeyapaul
粗粒度可重构阵列 (CGRA) 是流行的加速器，主要用于流媒体、过滤和解码应用程序。由于其高性能和高功效，CGRA 也可以成为加速通用应用循环的有前途的解决方案。然而，通用应用程序中的循环通常很复杂，例如具有完美和不完美嵌套的循环以及具有嵌套 if-then-else（条件）的循环。我们认为现有的执行分支和条件的硬件软件解决方案效率低下。为了在 CGRA 上高效执行复杂的循环，我们提出了一种硬件-软件混合解决方案：LASER——一种加速应用程序计算密集型循环的综合技术。在LASER中，编译器将复杂的循环进行转换，将其映射到CGRA，并以特定的方式将它们放置在内存中，以便硬件在运行时可以从正确的路径获取并执行指令。 LASER 的几何平均性能提高了 40.91%，利用率提高了 43.43%，能耗降低了 46%。
Coarse-Grained Reconfigurable Arrays (CGRAs) are popular accelerators predominantly used in streaming, filtering, and decoding applications. Due to their high performance and high power-efficiency, CGRAs can be a promising solution to accelerate the loops of general purpose applications also. However, the loops in general purpose applications are often complicated, like loops with perfect and imperfect nests and loops with nested if-then-else's (conditionals). We argue that the existing hardware-software solutions to execute branches and conditions are inefficient. In order to efficiently execute complicated loops on CGRAs, we present a hardware-software hybrid solution: LASER — a comprehensive technique to accelerate compute-intensive loops of applications. In LASER, compiler transforms complex loops, maps them to the CGRA, and lays them out in the memory in a specific manner, such that the hardware can fetch and execute the instructions from the right path at runtime. LASER achieves a geomean performance improvement of 40.91% and utilization of 43.43% with 46% lower energy consumption.