Before revision: Examining field-programmable gate array (FPGA ) bitstream is found to help detect known function blocks, which offers assistance and insight to analyze the circuit's system function. Our goal is to detect one or more than one function block in FPGA design from a complete bitstream by utilizing the latest deep learning techniques, which do not require manually designing features. To this end, in this paper, we propose a  deep learning-based FPGA function block detection method by transforming the bitstream into a three-channel color image .
After revision: In the context of various application scenarios and/or for the sake of strengthening FPGA security, the system functions of an FPGA design need to be analyzed, which can be achieved by systematically partitioning the FPGA's bitstream into manageable functional blocks and detecting their functionalities thereafter. In this paper, we propose a deep learning-based FPGA function block detection method by transforming the bitstream into a three-channel color image .