Code Generation and GPU Support

Generate C/C++ and CUDA®code and MEX functions, and run functions on a graphics processing unit (GPU)

MATLAB®Coder™生成ANSI-compliant C / c++代码和独立executables from Wavelet Toolbox™ functions that support C/C++ code generation. GPU Coder™ generates CUDA code and standalone executables for functions that support GPU code generation. Wavelet Toolbox also includes functions that can automatically run on a GPU. This support requires Parallel Computing Toolbox™.

This tables shows supported Wavelet Toolbox workflows.

Workflow C/C++ Code Generation GPU Code Generation GPU Acceleration
Time-Frequency ✓ ✓ ✓
Signal Analysis ✓ ✓ ✓
Image Analysis ✓ ✓ ✓
Denoising ✓
Orthogonal and Biorthogonal Filter Banks ✓

Highlighted Topics

Featured Examples

Denoise Signal Using Generated C Code

Denoise Signal Using Generated C Code

Generate C code containing Wavelet Toolbox functions for denoising.
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从CWT CUDA代码

从CWT CUDA代码

Learn how to generate CUDA Code for the CWT.
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Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi

Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi

The workflow to classify human electrocardiogram (ECG) signals using the Continuous Wavelet Transform (CWT) and a deep convolutional neural network (CNN). This example also provides information on how to generate and deploy the code and CNN for prediction on a Raspberry Pi target (ARM®-based device).
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Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning

Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning

Generate and deploy a CUDA® executable that classifies human electrocardiogram (ECG) signals using features extracted by the continuous wavelet transform (CWT) and a pretrained convolutional neural network (CNN).
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GPU Acceleration of Scalograms for Deep Learning

GPU Acceleration of Scalograms for Deep Learning

Accelerate scalogram computation using GPUs. The computed scalograms are used as the input features to deep convolution neural networks (CNN) for ECG and spoken digit classification.
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Wavelet Time Scattering with GPU Acceleration — Music Genre Classification

Wavelet Time Scattering with GPU Acceleration — Music Genre Classification

Accelerate the computation of wavelet scattering features using gpuArray and a parallelized "depth-first" version of wavelet time scattering. You must have a CUDA-enabled NVIDIA GPU with compute capability 3.0 or higher. See GPU Support by Release for details.
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Wavelet Time Scattering with GPU Acceleration — Spoken Digit Recognition

Wavelet Time Scattering with GPU Acceleration — Spoken Digit Recognition

Accelerate the computation of wavelet scattering features using gpuArray and a parallelized "depth-first" version of wavelet time scattering. You must have a CUDA-enabled NVIDIA GPU with compute capability 3.0 or higher. See GPU Support by Release for details. This example uses an NVIDIA Titan XP GPU with compute capability 6.1.
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Wavelet Toolbox Documentation

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