Deep Learning

MATLAB and Simulink for Embedded AI

Deploy machine learning and deep learning applications to embedded systems

With MATLAB and Simulink, you can design, simulate, test, verify, and deploy AI algorithms that enhance the performance and functionality of complex embedded systems.

Illustration of the Embedded AI workflow.

Using MATLAB and Simulink for Deployment to Embedded AI

Discover how you can prepare AI models and automatically generate code to deploy embedded AI applications to CPUs, GPUs, FPGAs, and more. Explore tutorials, examples, and videos for practical advice on embedded AI with MATLAB and Simulink.

Screenshot of a layered graph, calibration statistics, and validation results to optimize AI models for embedded deployment.

Deploy to CPUs and Microcontrollers

Generate portable, optimized C/C++ code from trained machine learning and deep learning models with MATLAB Coder and Simulink Coder.

Screenshot of C/C++ code in Simulink being deployed to images of a NVIDIA desktop and embedded GPU.

Deploy to GPUs

Generate optimized CUDA® code for trained deep learning networks with GPU Coder for deployment to desktops, servers, and embedded GPUs.

Deploy to FPGAs and SoCs

Prototype and implement deep learning networks on FPGAs and SoCs with Deep Learning HDL Toolbox. Generate custom deep learning processor IP cores and bitstreams with HDL Coder.

Screenshot of a layered graph, calibration statistics, and validation results to optimize AI models for embedded deployment.

AI Model Compression

Compress deep neural networks with quantization, projection, or pruning to reduce memory footprint and increase inference performance.