code generation and gpu support -凯发k8网页登录

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 the gpuarray (parallel computing toolbox) function to transfer data to the gpu and then call the (parallel computing toolbox) function to retrieve the output data from the gpu. for a list of audio toolbox functions that support execution on gpus, see . 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, see . you need matlab coder™ to generate c/c code.

gpu code generation. after you develop your application, you can generate optimized cuda^® code for nvidia^® gpus from matlab code. the code can be integrated 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, see . you need matlab coder and gpu coder™ to generate cuda code.

apps

	generate c code or mex function from matlab code
	generate gpu code from matlab code

functions

`codegen`	generate c/c code from matlab code
	transfer distributed array, composite array or gpuarray to local workspace
`gpuarray`	array stored on gpu

topics

(matlab coder)
generate c/c code from matlab code by using the codegen command.
run matlab functions on a gpu (parallel computing toolbox)
supply a gpuarray argument to automatically run functions on a gpu.
(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.
(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

featured examples

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.