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Predictive Compositional Method to Design and Re-optimize Complex Behavioral Dataflows
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ( IF 2.9 ) Pub Date : 2020-10-01 , DOI: 10.1109/tcad.2020.2966447
Shuangnan Liu , Francis Lau , Benjamin Carrion Schafer

In this article, we introduce an automatic stream computing reoptimization flow from ASICs to field-programmable gate arrays (FPGAs). Complex VLSI designs need to be prototyped and/or emulated on FPGAs. The main problem that we address in this article is that configurations optimized when targeting ASICs are often, as we will show in this article, highly un-optimal when remapped onto an FPGA. Thus, this article proposes a method to first generate a variety of dataflow configurations targeting an ASIC given multiple behavioral descriptions for high-level synthesis (HLS) and then, based on a compositional predictive model, automatically reoptimize the dataflow when mapped onto an FPGA. The experimental results show that our proposed method works well and that it is very fast.

中文翻译:

设计和重新优化复杂行为数据流的预测组合方法

在本文中,我们介绍了从 ASIC 到现场可编程门阵列 (FPGA) 的自动流计算重新优化流程。复杂的 VLSI 设计需要在 FPGA 上进行原型设计和/或仿真。我们在本文中解决的主要问题是,针对 ASIC 进行优化的配置,正如我们将在本文中展示的那样,在重新映射到 FPGA 上时通常非常不优化。因此,本文提出了一种方法,首先生成针对 ASIC 的各种数据流配置,给定用于高级综合 (HLS) 的多个行为描述,然后基于组合预测模型,在映射到 FPGA 时自动重新优化数据流。实验结果表明,我们提出的方法效果很好,而且速度非常快。
更新日期:2020-10-01
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