An RISC-V PPA-Fusion Cooperative Optimization Framework Based on Hybrid Strategies

Tianning Gao, Yifan Wang, Ming Zhu, Xiulong Wu, Dian Zhou, Zhaori Bi

Abstract: The optimization of RISC-V designs, encompassing both microarchitecture and CAD tool parameters, is a great challenge due to an extensive and high-dimensional search space. Conventional optimization methods, such as case-specific approaches and black-box optimization approaches, often fall short of addressing the diverse and complex nature of RISC-V designs. To achieve optimal results across various RISC-V designs, we propose the cooperative optimization framework (COF) that integrates multiple black-box optimizers, each specializing in different optimization problems. The COF introduces the landscape knowledge exchange mechanism (LKEM) to direct the optimizers to share their knowledge of the optimization problem. Moreover, the COF employs the dynamic computational resource allocation (DCRA) strategies to dynamically allocate computational resources to the optimizers. The DCRA strategies are guided by the optimizer efficiency evaluation (OEE) mechanism and a time series forecasting (TSF) model. The OEE provides real-time performance evaluations. The TSF model forecasts the optimization progress made by the optimizers, given the allocated computational resources. In our experiments, the COF reduced the cycle per instruction (CPI) of the Berkeley out-of-order machine (BOOM) by 15.36% and the power of Rocket-Chip by 12.84% without constraint violation compared to the respective initial designs.