Heterogeneous accelerator-rich (ACC-rich) platforms combining general-purpose cores and specialized HW accelerators (ACCs) promise high-performance and low-power streaming application deployments in a variety of domains, such as video analytics and software-defined radio. In order to benefit a domain of applications, a domain platform exploration tool must take advantage of structural and functional similarities across applications by allocating a common set of ACCs. A previous approach proposed a genetic domain exploration tool (GIDE) that applied a restrictive binding algorithm that mapped applications functions to monolithic accelerators. This approach suffered from a low average application throughput across and reduced platform generality. This article introduces a multigranularity-based domain design space exploration tool (MG-DmDSE) to improve both average application throughput as well as platform generality. The key contributions of MG-DmDSE are: 1) applying a multigranular decomposition of coarse-grained application functions into more granular compute kernels; 2) examining compute similarity between functions in order to provide more generic functions; 3) configuring monolithic ACCs by selectively bypassing compute elements within them during DSE to expose more functionality; and 4) speeding up MG-DmDSE platform allocation exploration through a greedy guided mutation (GGM) algorithm. To assess MG-DmDSE, both GIDE and MG-DmDSE were applied to applications in the OpenVX library. MG-DmDSE achieves an average $2.84\times $ greater application throughput compared to GIDE. Additionally, 87.5% of applications benefited from running on the platform produced by MG-DmDSE versus 50% from GIDE, which indicated increased platform generality. The generated MG-DmDSE platforms achieve an average of 61.8% logarithmic throughput improvement for unknown applications over GIDE. GGM results in saving 84.8% of the exploration time in MG-DmDSE with only 0.23% performance loss.

中文翻译：

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使用多粒度域 DSE (MG-DmDSE) 利用应用程序相似性生成统一平台


异构加速器丰富（ACC丰富）平台结合了通用内核和专用硬件加速器（ACC），可在视频分析和软件定义无线电等各种领域实现高性能和低功耗的流应用部署。为了使应用程序领域受益，领域平台探索工具必须通过分配一组通用的 ACC 来利用应用程序之间的结构和功能相似性。之前的方法提出了一种遗传域探索工具（GIDE），该工具应用限制性绑定算法将应用程序功能映射到单片加速器。这种方法的缺点是平均应用程序吞吐量较低并且平台通用性较低。本文介绍了一种基于多粒度的域设计空间探索工具 (MG-DmDSE)，以提高平均应用程序吞吐量和平台通用性。 MG-DmDSE 的主要贡献是：1）将粗粒度应用函数的多粒度分解应用到更细粒度的计算内核中； 2）检查函数之间的计算相似性以提供更通用的函数； 3) 通过在 DSE 期间有选择地绕过其中的计算元素来配置单片 ACC，以公开更多功能； 4) 通过贪婪引导突变（GGM）算法加速 MG-DmDSE 平台分配探索。为了评估 MG-DmDSE，GIDE 和 MG-DmDSE 均应用于 OpenVX 库中的应用程序。与 GIDE 相比，MG-DmDSE 的应用程序吞吐量平均提高了 2.84 美元\ 倍。此外，87.5% 的应用程序受益于在 MG-DmDSE 生成的平台上运行，而 GIDE 生成的应用程序只有 50%，这表明平台通用性有所提高。 与 GIDE 相比，生成的 MG-DmDSE 平台对于未知应用程序实现了平均 61.8% 的对数吞吐量改进。 GGM 结果在 MG-DmDSE 中节省了 84.8% 的探索时间，而性能损失仅为 0.23%。