CPU Code Generation from MATLAB Applications
Use a combination of MATLAB® Coder™, Simulink® Coder, and Embedded Coder® together with Deep Learning Toolbox™ to generate MEX or standalone CPU code that runs on desktop or embedded targets. You can deploy the generated standalone code that uses the Intel® MKL-DNN library or the ARM® Compute library. Alternatively, you can generate generic CPU code that does not call third-party library functions. You can also generate and deploy code that uses TensorFlow™ Lite models to perform inference.
|Generate C/C++ code from MATLAB code|
|Get the list of layers supported for code generation for a specific deep learning library|
|Load deep learning network model|
|Load network distribution discriminator for code generation (Da R2023a)|
|Create deep learning code generation configuration objects|
|Load TensorFlow Lite model (Da R2022a)|
|Compute deep learning network output for inference by using a TensorFlow Lite model (Da R2022a)|
|TensorFlow Lite model (Da R2022a)|
|MATLAB Coder||Generate C code or MEX function from MATLAB code|
- Networks and Layers Supported for Code Generation (MATLAB Coder)
Choose a convolutional neural network that is supported for your target processor.
- Load Pretrained Networks for Code Generation (MATLAB Coder)
dlnetworkobject for code generation.
- Code Generation for dlarray (MATLAB Coder)
Use deep learning arrays in MATLAB code intended for code generation.
- Generate Digit Images Using Variational Autoencoder on Intel CPUs (MATLAB Coder)
Generate code for a trained VAE dlnetwork to generate hand-drawn digits.
- Prerequisites for Deep Learning with TensorFlow Lite Models
Install products and configure environment for simulation and code generation with TensorFlow Lite models.
- Code Generation for Deep Learning on ARM Targets
This example shows how to generate and deploy code for prediction on an ARM®-based device without using a hardware support package.
- Deep Learning Prediction with ARM Compute Using codegen
This example shows how to use
codegento generate code for a Logo classification application that uses deep learning on ARM® processors.
- Deep Learning Code Generation on Intel Targets for Different Batch Sizes
This example shows how to use the
codegencommand to generate code for an image classification application that uses deep learning on Intel® processors.
- Generate C++ Code for Object Detection Using YOLO v2 and Intel MKL-DNN
This example shows how to generate C++ code for the YOLO v2 Object detection network on an Intel® processor.
- Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi
Classify human electrocardiogram signals on a Raspberry Pi® using scalograms and a deep convolutional neural network.
- Deploy Signal Segmentation Deep Network on Raspberry Pi
Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi.
- Generate Code and Deploy MobileNet-v2 Network to Raspberry Pi
This example shows how to generate C++ code that uses the MobileNet-v2 pretrained network for object prediction.
- Code Generation for Semantic Segmentation Application on ARM Neon Targets That Uses U-Net
Generate a static library that performs image segmentation by using the deep learning network U-Net on ARM targets.
- Code Generation for LSTM Network on Raspberry Pi
Generate code for a pretrained long short-term memory network to predict Remaining Useful Life (RUI) of a machine.
- Code Generation for LSTM Network That Uses Intel MKL-DNN
Generate code for a pretrained LSTM network that makes predictions for each step of an input timeseries.
- Cross Compile Deep Learning Code for ARM Neon Targets
Generate library or executable code on host computer for deployment on ARM hardware target.
- Generate INT8 Code for Deep Learning Network on Raspberry Pi (MATLAB Coder)
Generate code for deep learning network that performs inference computations in 8-bit integers.
- Generate Generic C/C++ Code for Sequence-to-Sequence Regression That Uses Deep Learning
Generate C/C++ code for a trained CNN that does not depend on any third-party libraries.
- Generate Code for LSTM Network and Deploy on Cortex-M Target (MATLAB Coder)
Generate a Processor-In-the-Loop (PIL) executable that runs on an STM32F746G-Discovery board.
- Generate Code for TensorFlow Lite (TFLite) Model and Deploy on Raspberry Pi
Generate code that uses a TensorFlow Lite model for inference.
- Deploy Classification Application Using Mobilenet-V3 TensorFlow Lite Model on Host and Raspberry Pi
Generate code for a classification segmentation application that uses Tensorflow Lite model.
- Deploy Semantic Segmentation Application Using TensorFlow Lite Model on Host and Raspberry Pi
Generate code for an image segmentation application that uses Tensorflow Lite model.
- Deploy Super Resolution Application That Uses TensorFlow Lite (TFLite) Model on Host and Raspberry Pi
Generate code for a super resolution application that uses a TFLite model for inference.
- Deploy Pose Estimation Application Using TensorFlow Lite Model (TFLite) Model on Host and Raspberry Pi
Simulate and generate code for a TensorFlow Lite model for 2D human pose estimation.