Wide-field fluorescence lifetime imaging (FLIM) is a promising technique for biomedical and clinic applications. Integrating with CMOS single-photon avalanche diode (SPAD) sensor arrays can lead to cheaper and portable real-time FLIM systems. However, the FLIM data obtained by such sensor systems often have sophisticated noise features. There is still a lack of fast tools to recover lifetime parameters from highly noise-corrupted fluorescence signals efficiently. This paper proposes a smart wide-field FLIM system containing a 192×128 COMS SPAD sensor and a field-programmable gate array (FPGA) embedded deep learning (DL) FLIM processor. The processor adopts a hardware-friendly and light-weighted neural network for fluorescence lifetime analysis, showing the advantages of high accuracy against noise, fast speed, and low power consumption. Experimental results demonstrate the proposed system's superior and robust performances, promising for many FLIM applications such as FLIM-guided clinical surgeries, cancer diagnosis, and biomedical imaging.