Efficient and Robust High-Level Synthesis Design Space Exploration through offline Micro-kernels Pre-characterization
Efficient and Robust High-Level Synthesis Design Space Exploration through offline Micro-kernels Pre-characterization
复制标题
通过离线微内核预表征进行高效、稳健的高级综合设计空间探索
DOI：
10.23919/date48585.2020.9116309
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Benjamin Carrión Schäfer
中科院分区：
文献类型：
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作者：
Zi Wang;Jianqi Chen;Benjamin Carrión Schäfer
这项工作提出了一种加速高级合成过程（HLS）设计空间探索（DSE）的方法，通过将微核预脱机并创建HLS的预测模型。相同的行为描述通常是通过在代码中指定为PRAGMAS的形式或合成指令中的合成选项的不同组合允许控制循环的阵列和功能。可探索的操作数量是基于脱机微内核的预性，为每个内核创建预测模型，并使用结果探索使用复合方法的新的看不见的行为描述。带有预示率的微核的微核，以进一步加快搜索过程。
This work proposes a method to accelerate the process of High-Level Synthesis (HLS) Design Space Exploration (DSE) by pre-characterizing micro-kernels offline and creating predictive models of these. HLS allows to generate different types of micro-architectures from the same untimed behavioral description. This is typically done by setting different combinations of synthesis options in the form or synthesis directives specified as pragmas in the code. This allows, e.g. to control how loops should be synthesized, arrays and functions. Unique combinations of these pragmas leads to micro-architectures with a unique area vs. performance/power trade-offs. The main problem is that the search space grows exponentially with the number of explorable operations. Thus, the main goal of efficient HLS DSE is to find the synthesis directives’ combinations that lead to the Pareto-optimal designs quickly. Our proposed method is based on the pre-characterization of micro-kernels offline, creating predictive models for each of the kernels, and using the results to explore a new unseen behavioral description using compositional methods. In addition, we make use of perceptual hashing to match new unseen micro-kernels with the pre-characterized micro-kernels in order to further speed up the search process. Experimental results show that our proposed method is orders of magnitude faster than traditional methods.