High-Level Synthesis (HLS) tools allow the generation of a large variety of hardware implementations from the same specification by setting different optimization directives. Each combination of HLS directives returns an implementation of the target application that is based on a particular microarchitecture. Designers are interested only in the subset of implementations that correspond to Pareto-optimal points in the performance versus cost design space. Finding this subset is hard because the relationship between the HLS directives and the Pareto-optimal implementations cannot be foreseen. Hence, designers must default to an exploration of the design space through many time-consuming HLS runs. We present a methodology that infers knowledge from past design explorations to identify high-quality directives for new target applications. To this end, we formulate a novel abstract representation of applications and their associated configuration spaces, introduce a similarity metric to compare quantitatively the configuration spaces of different applications, and a method to infer actionable information from a source space to a target space. The experimental results with the MachSuite benchmarks show that our approach retrieves close approximations of the Pareto frontier of best-performing implementations for the target application, in exchange for a small number of HLS runs.

中文翻译：

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在高级综合中利用先验知识进行有效的设计空间探索

高级综合 (HLS) 工具允许通过设置不同的优化指令从相同的规范生成大量不同的硬件实现。HLS 指令的每个组合都会返回基于特定微体系结构的目标应用程序的实现。设计人员只对与性能与成本设计空间中帕累托最优点相对应的实现子集感兴趣。找到这个子集很困难，因为无法预见 HLS 指令和帕累托最优实现之间的关系。因此，设计人员必须默认通过许多耗时的 HLS 运行来探索设计空间。我们提出了一种方法，可以从过去的设计探索中推断知识，以确定新目标应用程序的高质量指令。为此，我们制定了应用程序及其相关配置空间的新颖抽象表示，引入了相似性度量以定量比较不同应用程序的配置空间，以及一种从源空间到目标空间推断可操作信息的方法。MachSuite 基准测试的实验结果表明，我们的方法检索目标应用程序最佳性能实现的帕累托边界的近似值，以换取少量 HLS 运行。