Correlated Multi-objective Multi-fidelity Optimization for HLS Directives Design
Qi Sun1,a, Tinghuan Chen1, Siting Liu1, Jin Miao2, Jianli Chen3, Hao Yu4 and Bei Yu1,b
1The Chinese University of Hong Kong
2Synopsys
3Fudan University
4SusTech
acqsun@cse.cuhk.edu.hk
bbyu@cse.cuhk.edu.hk
ABSTRACT
High-level synthesis (HLS) tools have gained great attention in recent years because it emancipates engineers from the complicated and heavy hardware description language writing, by using high-level languages and HLS directives. However, previous works seem powerless, due to the time-consuming design processes, the contradictions among design objectives, and the accuracy difference between the three stages (fidelities). To find good HLS directives, in this paper, a novel correlated multiobjective non-linear optimization algorithm is proposed to explore the Pareto solutions while making full use of data from different fidelities. A non-linear Gaussian process is proposed to model relationships among the analysis reports from different fidelities for the same objective. For the first time, correlated multivariate Gaussian process models are introduced into this domain to characterize the complex relationships of multiple objectives in each design fidelity. A tree-based method is proposed to erase invalid solutions and obviously non-optimal solutions. Experimental results show that our non-linear and pioneering correlated models can approximate the Pareto-frontier of the directive design space in a shorter time with much better performance and good stability, compared with the state-of-the-art.
