DataMaster: A GNN-based Data Type Optimizer for Dataflow Design in FPGA

In the process of using FPGA to accelerate applications, we can obtain more efficient design by selecting appropriate data bit width and precision. However, the reasonable selection of data type needs to be determined by the designer, which often requires rich design experience and a lot of time. In this paper, we propose DataMaster, which is a GNN-based optimization framework for data types used in FPGA design. The purpose of DataMaster is to find the most suitable data bit width and precision for computing tasks at the operator level and finally obtain a hardware design with better performance and less resource consumption.