The presentation aims to show that modern deep learning models, developed by academic purposes can, in a simple way, be adapted to run efficiently on embedded accelerators. This will be exemplified with performance measurements for a DL model designed for a Nvidia desktop GPU (RTX 3090) running on a Texas Instruments C7x DSP (TDA4) for a real time application with a minimal loss of accuracy.