Leveraging Prior Knowledge for Effective Design-Space Exploration in High-Level Synthesis
Leveraging Prior Knowledge for Effective Design-Space Exploration in High-Level Synthesis
复制标题
利用先验知识进行高级综合中的有效设计空间探索
DOI：
10.1109/tcad.2020.3012750
复制
发表时间：
2020
影响因子：
2.9
通讯作者：
L. Pozzi
中科院分区：
文献类型：
--
作者：
Lorenzo Ferretti;Jihye Kwon;G. Ansaloni;G. D. Guglielmo;L. Carloni;L. Pozzi
高级合成（HLS）工具允许通过设置不同的优化指令从相同规范中生成各种各样的硬件实现。 HLS指令的每种组合都返回基于特定微体系结构的目标应用程序的实现。设计师只对与性能与成本设计空间相对应的实现子集有兴趣。发现此子集很困难，因为无法预见HLS指令与帕累托最佳实现之间的关系。因此，设计师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，该方法可以渗透到过去的设计探索中，以确定针对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关联配置空间的新型抽象表示，引入了一个相似性度量，以定量比较不同应用程序的配置空间，以及一种将可起诉信息从源空间推导的方法。 Machsuite基准测试的实验结果表明，我们的方法检索了目标应用程序最佳实现的帕累托前沿的近似值，以换取少量HLS运行。
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.
DOI：
10.1109/fccm.2018.00029
发表时间：
2018-04
期刊：
2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)
影响因子：
--
作者：
Steve Dai;Yuan Zhou;Hang Zhang;Ecenur Ustun;Evangeline F. Y. Young;Zhiru Zhang
通讯作者：
Steve Dai;Yuan Zhou;Hang Zhang;Ecenur Ustun;Evangeline F. Y. Young;Zhiru Zhang