METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD ICCAD Special Session Paper
Jinwook Jung, Andrew B. Kahng, Seungwon Kim, Ravi Varadarajan · 2021
In today's RTL-to-GDS flow domain, there is a lack of standards for reporting of design and tool metrics. Moreover, each tool or engine has its own set of parameters that can change outcomes and trade off PPA and other metrics. Thus, the study and optimization of impacts of parameter settings across the entire RTL- to-GDS tool chain has been largely ad hoc. In this paper, we first describe METRICS2.1, a proposed standard for RTL-to-GDS design tool and flow metrics. We then describe how data collected using a METRICS2.1 realization can be analyzed to give insight into flow tuning and fields of use for PPA optimization. Last, we discuss hyperparameter autotuning in the RTL-to-GDS flow. We present AutoTuner, which uses derivative-free optimization to handle challenges of non-differentiability and many local minima. An open repository based on METRICS2.1 has been established for sharing of reproducible, standardized metrics data, along with example implemented applications, to support academic and industrial research on machine learning for tool/flow tuning.