Predictive Compositional Method to Design and Reoptimize Complex Behavioral Dataflows

Abstract

In this article, we introduce an automatic stream computing reoptimization flow from ASICs to field-programmable gate arrays (FPGAs). Complex VLSI designs need to be prototyped and/or emulated on FPGAs. The main problem that we address in this article is that configurations optimized when targeting ASICs are often, as we will show in this article, highly un-optimal when remapped onto an FPGA. Thus, this article proposes a method to first generate a variety of dataflow configurations targeting an ASIC given multiple behavioral descriptions for high-level synthesis (HLS) and then, based on a compositional predictive model, automatically reoptimize the dataflow when mapped onto an FPGA. The experimental results show that our proposed method works well and that it is very fast.