Chen BAI Won William J. McCalla ICCAD Best Paper Award at ICCAD 2021

Congratulations to Chen BAI (a PhD student supervised by Prof. Bei Yu and Prof. Martin Wong) and co-authors (Qi Sun, Jianwang Zhai, Yuzhe Ma, Bei Yu, Martin Wong) won William J. McCalla ICCAD Best Paper Award (Front-End Award) at the ACM/IEEE International Conference on Computer-Aided Design (ICCAD 2021) for their paper “BOOM-Explorer: RISC-V BOOM Microarchitecture Design Space Exploration Framework”.

In memory of William J. McCalla for his contributions to ICCAD and his CAD technical work throughout his career, William J. McCalla ICCAD Best Paper Award is to recognize the best paper presented at ICCAD. The awards are split into three sections, two for the current year of the ICCAD conference and one for an ICCAD paper from 10 years prior. For the current year awards, one will be given for the best research paper covering the front-end of the design process, and one will be given for the back-end of the design process. The awards are jointly sponsored by IEEE Council on Electronic Design Automation (IEEE CEDA) and the ACM Special Interest Group on Design Automation (ACM SIGDA). The awards are decided by ICCAD Best Paper and Most Influential Awards Selection Committees and were first given in 2000.


The microarchitecture design of a processor has been increasingly difficult due to the large design space and time-consuming verification flow. Previously, researchers rely on prior knowledge and cycle-accurate simulators to analyze the performance of different microarchitecture designs but lack sufficient discussions on methodologies to strike a good balance between power and performance. This work proposes an automatic framework to explore microarchitecture designs of the RISC-V Berkeley Out-of-Order Machine (BOOM), termed as BOOM-Explorer, achieving a good trade-off on power and performance. Firstly, the framework utilizes an advanced microarchitecture-aware active learning (MicroAL) algorithm to generate a diverse and representative initial design set. Secondly, a Gaussian process model with deep kernel learning functions (DKL-GP) is built to characterize the design space. Thirdly, correlated multi-objective Bayesian optimization is leveraged to explore Pareto-optimal designs. Experimental results show that BOOM-Explorer can search for designs that dominate previous arts and designs developed by senior engineers in terms of power and performance within a much shorter time.


From left: Bei Yu, Martin Wong, Qi Sun, Chen Bai