Artificial intelligence (Deep Learning) is everywhere. Home appliances, automotive, entertainment systems, you name it, they are all packing AI capabilities. The space industry is no exception. Automated recognition of spacecraft and space junk using imaging plays an important role in securing space safety and space exploration. Although Deep Learning is the most successful solution for image-based object classification, for most real applications it also requires performant platforms like FPGAs and SoCs.
Designing Deep Learning networks for embedded devices like FPGAs and SoCs is challenging because of resource constraints, complexity of programming in Verilog or VHDL, and the hardware expertise needed for prototyping on an FPGA or SoC.
Learn how to prototype and deploy Deep Learning-based vision applications on FPGAs and SoCs using MATLAB. Starting with a pretrained model either trained in MATLAB or any framework of your choice, we demonstrate a workflow how to deploy the trained network for image recognition from MATLAB to the Xilinx Ultrascale+ MPSoC platform for inference using APIs from MATLAB.
Deep Learning algorithm engineers can quickly explore different networks and their performance on an FPGA or SoC directly from MATLAB. The workflow also enables hardware engineers to optimize and generate portable Verilog and VHDL Code that can be integrated with the rest of their application.