Time Series and Sequence Data Networks
R2026bYou can train and deploy networks to do time series classification, regression, and forecasting tasks by using long short-term memory (LSTM) networks. An LSTM is a type of recurrent neural network (RNN) that can learn long-term dependencies between time steps of sequence data. Learn about:
Support for LSTM networks.
How Deep Learning HDL Toolbox™ compiles the LSTM layer in a network.
How to deploy LSTM networks to target FPGA and SoC boards, then use Deep Learning HDL Toolbox and MATLAB® to retrieve the prediction results from the network.
Functions
| Configure deployment workflow for deep learning neural network |
| Configure interface to target board for workflow deployment |
Topics
- Support for Long Short-Term Memory Networks (Deep Learning HDL Toolbox)
Learn about Deep Learning HDL Toolbox support for long short-term memory (LSTM) networks.
Featured Examples
Run Sequence-to-Sequence Classification Networks with Projected Layers on FPGA
Improve network performance by using a projected layer network.
Run Sequence-to-Sequence Classification on Intel FPGA
Create, compile, and deploy a long short-term memory (LSTM) network trained on accelerometer data from human movement by using the HDL Coder™ Support Package for Intel® FPGA and SoC Devices. Use the deployed network to classify human activity based on sequence input data. Use MATLAB® to retrieve the prediction results from the target device.
