MLSBench: A Benchmark Set for Machine Learning based FPGA HLS Design Flows
MLSBench: A Benchmark Set for Machine Learning based FPGA HLS Design Flows
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MLSBench：基于机器学习的 FPGA HLS 设计流程的基准集
DOI：
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发表时间：
2022
期刊：
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通讯作者：
D. Bhatia
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作者：
Pingakshya Goswami;Masoud Shahshahani;D. Bhatia
高级合成（HLS）正在成为开始大型基于FPGA的设计项目的DefaTso标准。 FPGA设计流完全采用基于HLS的方法，因此几乎没有硬件设计技能的软件工程师可以轻松使用其工具。高级合成（HLS）期间使用的行为描述完全独立于技术，因此设计师很难解释合成选项的变化如何影响所得电路。各个行业和学术界的研究人员正在基于机器学习的高级合成（HLS）工具设计领域进行研究，其中可以使用各种ML技术来预测结果的质量（QOR）。所有这些作品中最大的挑战之一是开源HLS设计的可用性，设计师可以在这些设计上进行训练和预测其模型。基准的产生是一个耗时的过程，缺乏标准基准的可用性可阻止各种拟议模型之间的公平比较。在本文中，我们提出了一种方法，用于生成具有各种设计变化的各种设计的方法。我们创建了一个数据集，该数据集通过C/C ++和系统C编写的6000多个可合成的FPGA HLS设计。我们提供了生成的基准测试的详细统计分析。数据集可用于公共使用。我们已经证明了我们的数据集在涉及快速基于模型的设计空间探索的案例研究中的使用。
High-Level Synthesis (HLS) is becoming a defacto standard for starting large FPGA-based design projects. FPGA design flows are completely embracing HLS based methodologies so that software engineers with almost no hardware design skills can easily use their tools. Behavioral descriptions used during the high-level synthesis (HLS) are completely technology-independent, making it hard for designers to interpret how changes in the synthesis options affect the resultant circuit. Researchers across industry and academia are performing research in the field of machine-learning-based predictive high-level synthesis (HLS) tool design, where the quality of results (QOR) can be predicted using various ML techniques. One of the greatest challenges in all these works is the availability of open-source HLS designs on which the designers can train and predict their models. Generation of benchmarks is a time-consuming process and lack of availability of standard benchmarks prevents fair comparison among various proposed models. In this paper, we propose a methodology for generating diverse designs with various variations from a single design. We have created a data-set of more than 6000 synthesizable FPGA HLS designs written in C/C++ and System C. We provide a detailed statistical analysis of the generated benchmarks. The data set is available for public use. We have demonstrated the use of our data-set in case studies that involve quick model-based design space exploration.