Learn how to prototype and deploy deep learning-based vision applications on FPGAs using MATLAB. Designing deep learning networks for embedded devices is challenging because of processing and memory resource constraints. FPGAs present an even greater challenge due to the complexity of programming in Verilog or VHDL, and the hardware expertise needed for prototyping on an FPGA. This talk illustrates a workflow to facilitate the design and deployment of these applications to FPGAs using pre-built bitstreams without the need for much hardware expertise. Starting with a pre-trained model trained either in MATLAB or any framework of your choice, we demonstrate the workflow to prototype and deploy the trained network from MATLAB to an FPGA. We illustrate this flow using a deep learning network for image recognition and deploy it to the Xilinx MPSoC board for inference using APIs from MATLAB. This demonstrates how deep learning algorithm engineers can quickly explore different networks and their performance on an FPGA from MATLAB.