Code Generation and GPU Support

Generate portable C/C++/MEX functions and use GPUs to deploy or accelerate processing

Audio Toolbox™ includes support to accelerate prototyping in MATLAB^®and to generate code for deployment.

GPU Code Acceleration.To speed up your code while prototyping, Audio Toolbox includes functions that can execute on a Graphics Processing Unit (GPU). You can use thegpuArray(Parallel Computing Toolbox)function to transfer data to the GPU and then call thegather(Parallel Computing Toolbox)function to retrieve the output data from the GPU. For a list of Audio Toolbox functions that support execution on GPUs, seeFunction List (gpuArraysupport). You need Parallel Computing Toolbox™ to enable GPU support.

C/C++ Code Generation.After you develop your application, you can generate portable C/C++ source code, standalone executables, or standalone applications from your MATLAB code. C/C++ code generation enables you to run your simulation on machines that do not have MATLAB installed and to speed up processing while you work in MATLAB. For a list of Audio Toolbox functions that support C/C++ code generation, seeFunction List (C/C++ Code Generation). You needMATLAB Coder™to generate C/C++ code.

GPU Code Generation.After you develop your application, you can generate optimized CUDA^®code for NVIDIA^®从MATLAB代码gpu。可以集成的代码into your project as source code, static libraries, or dynamic libraries, and can be used for prototyping on GPUs. You can also use the generated CUDA code within MATLAB to accelerate computationally intensive portions of your MATLAB code in machine learning, deep learning, or other applications. For a list of Audio Toolbox functions that support GPU code generation, seeFunction List (GPU Code Generation). You needMATLAB Coderand GPU Coder™ to generate CUDA code.

Apps

MATLAB Coder	Generate C code or MEX function fromMATLABcode
GPU Coder	Generate GPU code fromMATLABcode

Functions

`codegen`	Generate C/C++ code fromMATLABcode
`gather`	Transfer distributed array or gpuArray to local workspace
`gpuArray`	Array stored on GPU

Topics

Generate C Code at the Command Line(MATLAB Coder)
Generate C/C++ code from MATLAB code by using thecodegencommand.
Run MATLAB Functions on a GPU(Parallel Computing Toolbox)
Supply agpuArrayargument to automatically run functions on a GPU.
Prerequisites for Deep Learning with MATLAB Coder(MATLAB Coder)
Install products and configure environment for code generation for deep learning networks.
GPU Computing Requirements(Parallel Computing Toolbox)
Support for NVIDIA GPU architectures.
识别和显示口头指令在Android Device(Simulink Support Package for Android Devices)
This example shows how to use the Simulink® Support Package for Android™ Devices to deploy a deep learning algorithm that recognizes and displays commands spoken through your Android device such as a phone or tablet.

Related Information

Generate C Code from MATLAB Code
GPU Computing tutorial

Featured Examples

Speech Command Recognition Code Generation with Intel MKL-DNN Using Simulink

Use Embedded Coder^®in Simulink^®and Intel^®MKL-DNN to deploy feature extraction and a convolutional neural network for speech command recognition on Intel processors.

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Speech Command Recognition on Raspberry Pi Using Simulink

Deploy feature extraction and a convolutional neural network for speech command recognition on Raspberry Pi™.

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Accelerate Audio Deep Learning Using GPU-Based Feature Extraction

Leverage GPUs for feature extraction to decrease the time required to train an audio deep learning model.

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Keyword Spotting in Noise Code Generation on Raspberry Pi

Demonstrates code generation for keyword spotting using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction on Raspberry Pi™. MATLAB® Coder™ with Deep Learning Support enables the generation of a standalone executable (.elf) file on Raspberry Pi. Communication between MATLAB® (.mlx) file and the generated executable file occurs over asynchronous User Datagram Protocol (UDP). The incoming speech signal is displayed using a timescope. A mask is shown as a blue rectangle surrounding spotted instances of the keyword, YES. For more details on MFCC feature extraction and deep learning network training, visit Keyword Spotting in Noise Using MFCC and LSTM Networks.

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Keyword Spotting in Noise Code Generation with Intel MKL-DNN

Generate code to spot keywords using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction.

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Speech Command Recognition Code Generation with Intel MKL-DNN

Deploy feature extraction and a convolutional neural network (CNN) for speech command recognition on Intel® processors. To generate the feature extraction and network code, you use MATLAB® Coder™ and the Intel® Math Kernel Library for Deep Neural Networks (MKL-DNN). In this example, the generated code is a MATLAB executable (MEX) function, which is called by a MATLAB script that displays the predicted speech command along with the time domain signal and auditory spectrogram. For details about audio preprocessing and network training, see Speech Command Recognition Using Deep Learning.

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Speech Command Recognition Code Generation on Raspberry Pi

Deploy feature extraction and a convolutional neural network (CNN) for speech command recognition to Raspberry Pi™. To generate the feature extraction and network code, you use MATLAB Coder™, MATLAB® Support Package for Raspberry Pi Hardware, and the ARM® Compute Library. In this example, the generated code is an executable on your Raspberry Pi, which is called by a MATLAB script that displays the predicted speech command along with the signal and auditory spectrogram. Interaction between the MATLAB script and the executable on your Raspberry Pi is handled using the user datagram protocol (UDP). For details about audio preprocessing and network training, see Speech Command Recognition Using Deep Learning.

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Acoustics-Based Machine Fault Recognition Code Generation with Intel MKL-DNN

Generate a MATLAB standalone executable for acoustics-based machine fault recognition.

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Acoustics-Based Machine Fault Recognition Code Generation on Raspberry Pi

Generate a Raspberry Pi standalone executable for acoustics-based machine fault recognition.

Open Live Script