Artificial Intelligence (AI) has made machine learning techniques unavoidable in many systems to gain insights to predict future behavior. To collect these insights in real-time these algorithms should be deployed on embedded platforms like CPU, GPU or System-on-Chip (CPU + FPGA).
The development of these systems remains complex by the underlying theory and the mapping to programming languages like C, CUDA and VHDL. MATLAB offers an integrated framework dedicated to the design and evaluation of your machine learning algorithms. Thanks to its code generation capability adopted by many manufacturers, it simplifies the deployment on embedded targets (CPU, GPU, SoC).
Discover in this webinar the process to follow to achieve this deployment and the tools at your disposal to converge towards an efficient implementation.