Deep Learning with Simulink

Extend deep learning workflows using Simulink

Implement deep learning functionality in Simulink^® models by using blocks from the Deep Neural Networks, Python Neural Networks, and Deep Learning Layers block libraries, included in the Deep Learning Toolbox™, or by using the Deep Learning Object Detector block from the Analysis & Enhancement block library included in the Computer Vision Toolbox™.

To generate a Simulink model that uses the Deep Learning Layers block library to represent a network, use the exportNetworkToSimulink function.

Some deep learning functionality in Simulink uses a MATLAB Function block that requires a supported compiler. For most platforms, a default C compiler is supplied with the MATLAB^® installation. When using C++ language, you must install a compatible C++ compiler. To see a list of supported compilers, open Supported and Compatible Compilers, click the tab that corresponds to your operating system, find the Simulink Product Family table, and go to the For Model Referencing, Accelerator mode, Rapid Accelerator mode, and MATLAB Function blocks column. If you have multiple MATLAB-supported compilers installed on your system, you can change the default compiler using the mex -setup command. See Change Default Compiler.

Functions

exportNetworkToSimulink Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (Since R2024b)

Blocks

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Networks

Image Classifier	Classify data using a trained deep learning neural network (Since R2020b)
Predict	Predict responses using a trained deep learning neural network (Since R2020b)
Stateful Classify	Classify data using a trained deep learning recurrent neural network (Since R2021a)
Stateful Predict	Predict responses using a trained recurrent neural network (Since R2021a)
Deep Learning Object Detector	Detect objects using trained deep learning object detector (Since R2021b)
TensorFlow Model Predict	Predict responses using pretrained Python TensorFlow model (Since R2024a)
PyTorch Model Predict	Predict responses using pretrained Python PyTorch model (Since R2024a)
ONNX Model Predict	Predict responses using pretrained Python ONNX model (Since R2024a)
Custom Python Model Predict	Predict responses using pretrained custom Python model (Since R2024a)

Activation Layers

Clipped ReLU Layer	Clipped Rectified Linear Unit (ReLU) layer (Since R2024b)
GELU Layer	Gaussian error linear unit (GELU) layer (Since R2024b)
Leaky ReLU Layer	Leaky rectified linear unit (ReLU) layer (Since R2024b)
ReLU Layer	Rectified linear unit (ReLU) layer (Since R2024b)
Sigmoid Layer	Sigmoid layer (Since R2024b)
Softmax Layer	Softmax layer (Since R2024b)
Tanh Layer	Hyperbolic tangent (tanh) layer (Since R2024a)

Combination Layers

Addition Layer	Addition layer (Since R2024b)
Concatenation Layer	Concatenation layer (Since R2024b)
Depth Concatenation Layer	Depth concatenation layer (Since R2024b)
Multiplication Layer	Multiplication layer (Since R2024b)

Convolution and Fully Connected Layers

Convolution 1D Layer	1-D convolutional layer (Since R2024b)
Convolution 2D Layer	2-D convolutional layer (Since R2024b)
Convolution 3D Layer	3-D convolutional layer (Since R2024b)
Fully Connected Layer	Fully connected layer (Since R2024b)

Input Layer Normalizations

Rescale-Symmetric 1D	1-D input layer with `rescale-symmetric` normalization (Since R2024b)
Rescale-Symmetric 2D	2-D input layer with `rescale-symmetric` normalization (Since R2024b)
Rescale-Symmetric 3D	3-D input layer with `rescale-symmetric` normalization (Since R2024b)
Rescale-Zero-One 1D	1-D input layer with `rescale-zero-one` normalization (Since R2024b)
Rescale-Zero-One 2D	2-D input layer with `rescale-zero-one` normalization (Since R2024b)
Rescale-Zero-One 3D	3-D input layer with `rescale-zero-one` normalization (Since R2024b)
Zerocenter 1D	1-D input layer with `zerocenter` normalization (Since R2024b)
Zerocenter 2D	2-D input layer with `zerocenter` normalization (Since R2024b)
Zerocenter 3D	3-D input layer with `zerocenter` normalization (Since R2024b)
Zscore 1D	1-D input layer with `zscore` normalization (Since R2024b)
Zscore 2D	2-D input layer with `zscore` normalization (Since R2024b)
Zscore 3D	3-D input layer with `zscore` normalization (Since R2024b)

Normalization Layers

Batch Normalization Layer	Batch normalization layer (Since R2024b)
Layer Normalization Layer	Layer normalization layer (Since R2024b)

Pooling Layers

Average Pooling 1D Layer	1-D average pooling layer (Since R2024b)
Average Pooling 2D Layer	2-D average pooling layer (Since R2024b)
Average Pooling 3D Layer	3-D average pooling layer (Since R2024b)
Global Average Pooling 1D Layer	1-D global average pooling layer (Since R2024b)
Global Average Pooling 2D Layer	2-D global average pooling layer (Since R2024b)
Global Average Pooling 3D Layer	3-D global average pooling layer (Since R2024b)
Global Max Pooling 1D Layer	1-D global max pooling layer (Since R2024b)
Global Max Pooling 2D Layer	2-D global max pooling layer (Since R2024b)
Global Max Pooling 3D Layer	3-D global max pooling layer (Since R2024b)
Max Pooling 1D Layer	1-D max pooling layer (Since R2024b)
Max Pooling 2D Layer	2-D max pooling layer (Since R2024b)
Max Pooling 3D Layer	3-D max pooling layer (Since R2024b)

Sequence Layers

Flatten Layer	Flatten layer (Since R2024b)
GRU Layer	Gated recurrent unit (GRU) layer for recurrent neural network (RNN) (Since R2025a)
GRU Projected Layer	Gated recurrent unit (GRU) projected layer for recurrent neural network (RNN) (Since R2025a)
LSTM Layer	Long short-term memory (LSTM) layer for recurrent neural network (RNN) (Since R2024b)
LSTM Projected Layer	Long short-term memory (LSTM) projected layer for recurrent neural network (RNN) (Since R2024b)

Utility Layers

Dropout Layer

Dropout layer (Since R2024b)

Topics

Deep Learning Layer Blocks

List of Deep Learning Layer Blocks and Subsystems
Discover all the deep learning layer blocks and subsystems in Simulink.
Implement Unsupported Deep Learning Layer Blocks
This example shows how to implement layers using Simulink blocks or MATLAB code in a MATLAB Function block.

Images

Classify Images in Simulink Using GoogLeNet
This example shows how to classify an image in Simulink® using the Image Classifier block.
Acceleration for Simulink Deep Learning Models
Improve simulation speed with accelerator and rapid accelerator modes.
Lane and Vehicle Detection in Simulink Using Deep Learning
This example shows how to use deep convolutional neural networks inside a Simulink® model to perform lane and vehicle detection.
Classify ECG Signals in Simulink Using Deep Learning
This example shows how to use wavelet transforms and a deep learning network within a Simulink (R) model to classify ECG signals.
Classify Images in Simulink with Imported TensorFlow Network
Import a pretrained TensorFlow™ network using importTensorFlowNetwork, and then use the Predict block for image classification in Simulink.

Sequences

Predict and Update Network State in Simulink
This example shows how to predict responses for a trained recurrent neural network in Simulink® by using the Stateful Predict block.
Classify and Update Network State in Simulink
This example shows how to classify data for a trained recurrent neural network in Simulink® by using the Stateful Classify block.
Speech Command Recognition in Simulink
Detect the presence of speech commands in audio using a Simulink model.
Time Series Prediction in Simulink Using Deep Learning Network
This example shows how to use an LSTM deep learning network inside a Simulink® model to predict the remaining useful life (RUL) of an engine.
Simulate Calorie Burn Using Neural Network in Simulink
This example shows how to include a simple fully connected neural network in a Simulink® model that predicts calorie burn when given five time steps of sensor readings from a smart watch.
Battery State of Charge Estimation Using Deep Learning
Define requirements, prepare data, train deep learning networks, verify robustness, integrate networks into Simulink, and deploy models. (Since R2024b)
Physical System Modeling Using LSTM Network in Simulink
This example shows how to create a reduced order model (ROM) that acts as a virtual sensor in a Simulink® model using a long short-term memory (LSTM) neural network.
Classify Motor Faults Using Deep Learning
This example shows how to train a deep learning model to classify faults in a permanent magnet synchronous motor (PMSM) using simulated data across various revolutions per minute (RPM). (Since R2025a)
Improve Performance of Deep Learning Simulations in Simulink
This example shows how to use code generation to improve the performance of deep learning simulations in Simulink®.

Reinforcement Learning

Control Water Level in a Tank Using a DDPG Agent (Reinforcement Learning Toolbox)
Train a controller using reinforcement learning with a plant modeled in Simulink as the training environment.
Train DDPG Agent for Adaptive Cruise Control (Reinforcement Learning Toolbox)
Train a DDPG agent for an adaptive cruise control application.
Train DQN Agent for Lane Keeping Assist Using Parallel Computing (Reinforcement Learning Toolbox)
Train a DQN agent for an automated driving application using parallel computing.
Train DDPG Agent for Path-Following Control (Reinforcement Learning Toolbox)
Train a DDPG agent for lane following control.

Python Coexecution

Classify Images Using TensorFlow Model Predict Block
Classify images using TensorFlow Model Predict block.
Classify Images Using ONNX Model Predict Block
Classify images using ONNX Model Predict block.
Classify Images Using PyTorch Model Predict Block
Classify images using PyTorch Model Predict block.
Predict Responses Using TensorFlow Model Predict Block
Predict Responses Using TensorFlow Model Predict block.
Predict Responses Using ONNX Model Predict Block
Predict Responses Using ONNX Model Predict block.
Predict Responses Using PyTorch Model Predict Block
Predict Responses Using PyTorch Model Predict block.
Predict Responses Using Custom Python Model in Simulink (Statistics and Machine Learning Toolbox)
This example shows how to use the Custom Python Model Predict (Statistics and Machine Learning Toolbox) block for prediction in Simulink®.

Code Generation

Deep Learning Code Generation from Simulink Applications
Generate C/C++ and GPU code for deployment on desktop or embedded targets
Export Network to FMU
This example shows how to export a trained network as a Functional Mock-up Unit (FMU). (Since R2023b)

Featured Examples

Lane and Vehicle Detection in Simulink Using Deep Learning

Use deep convolutional neural networks inside a Simulink® model to perform lane and vehicle detection. This example takes the frames from a traffic video as an input, outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle, and detects vehicles in the frame.

Open Script

Classify ECG Signals in Simulink Using Deep Learning

Use wavelet transforms and a deep learning network within a Simulink (R) model to classify ECG signals. This example uses the pretrained convolutional neural network from the Classify Time Series Using Wavelet Analysis and Deep Learning example of the Wavelet Toolbox™ to classify ECG signals based on images from the CWT of the time series data. For information on training, see Classify Time Series Using Wavelet Analysis and Deep Learning (Wavelet Toolbox).

Open Script

Time Series Prediction in Simulink Using Deep Learning Network

Use an LSTM deep learning network inside a Simulink® model to predict the remaining useful life (RUL) of an engine. You include the network inside the Simulink model by using a Stateful Predict block, which predicts the RUL at every simulation step.

Open Live Script

Battery State of Charge Estimation in Simulink Using Feedforward Neural Network

Use a feedforward deep learning network inside a Simulink® model to predict the state of charge (SOC) of a battery. You include the network inside the Simulink model by using a Predict block, which predicts the SOC at every simulation step.

Open Live Script

New

Profile Deep Learning Network for Deployment

Profile a deep learning network in Simulink. Profiling your network before training is a good way of checking that the network meets your requirements, before spending time on training. This example shows how to check:

Since R2025a
Open Live Script