Machine learning (ML) has been widely used to improve the predictability of EDA tools. The use of CAD tools that express designs at higher levels of abstraction makes machine learning even more important to highlight the performance of various design steps. 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. FPGA design flows are completely embracing HLS based methodologies so that software engineers with almost no hardware design skills can easily use their tools. HLS tools allow design space exploration by modifying synthesis options, however, they lack accuracy in the Quality of Results (QoR) reported right after HLS. This lack of correctness results in sub-optimal designs with problems in timing closure. This paper presents a robust ML based design flow that can accurately predict post-route QoR for a given behavioral description without the need to synthesize the design. The model is an important design exploration tool where a designer can quickly view the impact on overall design quality when local and global optimization directives are changed. The proposed methodology presents two strong advantages: (i) Accurate prediction of the design quality (QoR), and (ii) complete elimination of the need to execute high-level synthesis for each design option. We predict three post route parameters, (i). Area, (ii). Latency and (iii). Clock Period of a design just by analyzing the high level behavioral code and some intermediate representation codes. We have integrated the methodology with Xilinx HLS tools and have demonstrated accurate estimation on a variety of FPGA families. Our estimated results are within 10\% of actual computed values

翻译：机器学习 (ML) 已被广泛用于提高 EDA 工具的可预测性。 使用显示高抽象度设计设计的 CAD 工具使得机器学习更加重要, 以突出各种设计步骤的性能。 高级合成(HLS) 期间使用的行为描述完全是技术独立的, 使得设计者很难解释合成选项的变化如何影响结果电路。 FPGA 设计流完全包含基于 HLS 的方法, 使得几乎没有硬件设计技能的软件工程师可以很容易地使用其工具。 HLS 工具允许通过修改合成选项来设计空间探索。 但是, 它们在HLS 之后报告的结果质量(QoR) 缺乏准确性。 高质量设计(QoR) 的次优化性描述结果(i) 以基于 MLLLE 设计流的动态流来准确预测后的结果, 而不需要对设计进行综合。 模型是一种重要的设计探索工具, 设计方能快速地观察当地和全球优化指令对总体设计质量的影响。 拟议的方法提供了两种强的优势: C级设计(i) 和地区设计后期的预估值(i) 。