Deep Network Quantizer
Description
Use the Deep Network Quantizer app to reduce the memory requirement of a deep neural network by quantizing weights, biases, and activations of layers to 8-bit scaled integer data types. Using this app, you can:
Visualize the dynamic ranges of convolution layers in a deep neural network.
Select individual network layers to quantize.
Assess the performance of a quantized network.
Generate GPU code to deploy the quantized network using GPU Coder™.
Generate HDL code to deploy the quantized network to an FPGA using Deep Learning HDL Toolbox™.
Generate C++ code to deploy the quantized network to an ARM Cortex-A microcontroller using MATLAB® Coder™.
Generate a simulatable quantized network that you can explore in MATLAB without generating code or deploying to hardware.
This app requires Deep Learning Toolbox Model Quantization Library. To learn about the products required to quantize a deep neural network, see Quantization Workflow Prerequisites.
Open the Deep Network Quantizer App
MATLAB command prompt: Enter
deepNetworkQuantizer
.MATLAB toolstrip: On the Apps tab, under Machine Learning and Deep Learning, click the app icon.
Examples
Quantize Network for GPU Deployment
To explore the behavior of a neural network with quantized convolution layers, use the Deep Network Quantizer app. This example quantizes the learnable parameters of the convolution layers of the squeezenet
neural network after retraining the network to classify new images.
This example uses a DAG network with the GPU execution environment.
Load the network to quantize into the base workspace.
load squeezenetmerch
net
net = DAGNetwork with properties: Layers: [68×1 nnet.cnn.layer.Layer] Connections: [75×2 table] InputNames: {'data'} OutputNames: {'new_classoutput'}
Define calibration and validation data.
The app uses calibration data to exercise the network and collect the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network and the dynamic ranges of the activations in all layers of the network. For the best quantization results, the calibration data must be representative of inputs to the network.
The app uses the validation data to test the network after quantization to understand the effects of the limited range and precision of the quantized learnable parameters of the convolution layers in the network.
In this example, use the images in the MerchData
data set. Define an augmentedImageDatastore
object to resize the data for the network. Then, split the data into calibration and validation data sets.
unzip('MerchData.zip'); imds = imageDatastore('MerchData', ... 'IncludeSubfolders',true, ... 'LabelSource','foldernames'); [calData, valData] = splitEachLabel(imds,0.7,'randomized'); aug_calData = augmentedImageDatastore([227 227],calData); aug_valData = augmentedImageDatastore([227 227],valData);
At the MATLAB command prompt, open the app.
deepNetworkQuantizer
In the app, click New and select Quantize a network
.
The app verifies your execution environment. For more information, see Quantization Workflow Prerequisites.
In the dialog, select the execution environment and the network to quantize from the base workspace. For this example, select a GPU execution environment and net - DAGnetwork
.
Decide whether to use the Network Preparation option. Network preparation modifies your neural network to improve performance and avoid error conditions in the quantization workflow. These modifications include conversion of your network to a dlnetwork
object. For more information, see prepareNetwork
.
For this example, deselect the Prepare network for quantization check box so the network remains a DAGNetwork
object. This workflow uses the default metric function for validation, which is only supported for DAGNetwork
and SeriesNetwork
objects.
The app displays the layer graph of the selected network.
In the Calibrate section of the toolstrip, under Calibration Data, select the augmentedImageDatastore
object from the base workspace containing the calibration data, aug_calData
. Select Calibrate.
The Deep Network Quantizer uses the calibration data to exercise the network and collect range information for the learnable parameters in the network layers.
When the calibration is complete, the app displays a table containing the weights and biases in the convolution and fully connected layers of the network as well as the dynamic ranges of the activations in all layers of the network and their minimum and maximum values during the calibration. To the right of the table, the app displays histograms of the dynamic ranges of the parameters.
In the Quantize column of the table, indicate whether to quantize the learnable parameters in the layer. Layers that are not quantized remain in single precision after quantization.
In the Quantize section of the toolstrip, under Quantize, select the MinMax exponent scheme. Click Quantize.
The Deep Network Quantizer quantizes the weights, activations, and biases of convolution layers in the network to scaled 8-bit integer data types. The app updates the histogram of the dynamic ranges of the parameters. The gray regions of the histograms indicate data that cannot be represented by the quantized representation. For more information on how to interpret these histograms, see Quantization of Deep Neural Networks.
In the Validate section of the toolstrip, under Validation Data, select the augmentedImageDatastore
object from the base workspace containing the validation data, aug_valData
.
In the Validate section of the toolstrip, under Validation Options, select the Default metric function.
Click Validate. The app uses the validation data to exercise the network. The app determines a default metric function to use for the validation based on the type of network that is being quantized. For a classification network, the app uses the top-1 accuracy metric.
When the validation is complete, the app displays the results of the validation, including:
Metric function used for validation
Result of the metric function before and after quantization
Memory requirement of the network before and after quantization (MB)
To use a metric function other than the default Top-1 accuracy metric, define a custom metric function. Save the custom metric function in a local file.
function accuracy = hComputeModelAccuracy(predictionScores, net, dataStore) %% Computes model-level accuracy statistics % Load ground truth. tmp = readall(dataStore); groundTruth = tmp.response; % Compare predicted label with actual ground truth. predictionError = {}; for idx=1:numel(groundTruth) [~, idy] = max(predictionScores(idx,:)); yActual = net.Layers(end).Classes(idy); predictionError{end+1} = (yActual == groundTruth(idx)); %#ok end % Sum all prediction errors. predictionError = [predictionError{:}]; accuracy = sum(predictionError)/numel(predictionError); end
To revalidate the network using this custom metric function, under Validation Options, enter the name of the custom metric function, hComputeModelAccuracy
. Select Add to add hComputeModelAccuracy
to the list of metric functions available in the app. Select hComputeModelAccuracy
as the metric function to use.
The custom metric function must be on the path. If the metric function is not on the path, this step causes an error.
Select Validate.
The app quantizes the network and displays the validation results for the custom metric function.
The app displays only scalar values in the validation results table. To view the validation results for a custom metric function with nonscalar output, export the dlquantizer
object as described below, then validate it using the validate
function in the MATLAB command window.
If the performance of the quantized network is not satisfactory, you can choose to not quantize some layers by clearing the layer in the table. You can also explore the effects of choosing a different exponent selection scheme for quantization in the Quantize menu. To see the effects of these changes, quantize and validate the network again.
After calibrating and quantizing the network, you can choose to export the quantized network or the dlquantizer
object. Select the Export button. In the drop-down list, select from the following options:
Export Quantized Network — Add the quantized network to the base workspace. This option exports a simulatable quantized network that you can explore in MATLAB without deploying to hardware.
Export Quantizer — Add the
dlquantizer
object to the base workspace. You can save thedlquantizer
object and use it for further exploration in the Deep Network Quantizer app or at the command line, or use it to generate code for your target hardware.Generate Code — Open the GPU Coder app and generate GPU code from the quantized neural network. Generating GPU code requires a GPU Coder™ license.
Quantize Network for CPU Deployment
This example uses:
- Deep Learning ToolboxDeep Learning Toolbox
- MATLAB CoderMATLAB Coder
- Embedded CoderEmbedded Coder
- Deep Learning Toolbox Model Quantization LibraryDeep Learning Toolbox Model Quantization Library
- MATLAB Support Package for Raspberry Pi HardwareMATLAB Support Package for Raspberry Pi Hardware
- MATLAB Coder Interface for Deep LearningMATLAB Coder Interface for Deep Learning
To explore the behavior of a neural network with quantized convolution layers, use the Deep Network Quantizer app. This example quantizes the learnable parameters of the convolution layers of the squeezenet
neural network after retraining the network to classify new images.
This example uses a DAG network with the CPU execution environment.
Load the network to quantize into the base workspace.
load squeezenetmerch
net
net = DAGNetwork with properties: Layers: [68×1 nnet.cnn.layer.Layer] Connections: [75×2 table] InputNames: {'data'} OutputNames: {'new_classoutput'}
Define calibration and validation data.
The app uses calibration data to exercise the network and collect the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network and the dynamic ranges of the activations in all layers of the network. For the best quantization results, the calibration data must be representative of inputs to the network.
The app uses the validation data to test the network after quantization to understand the effects of the limited range and precision of the quantized learnable parameters of the convolution layers in the network.
In this example, use the images in the MerchData
data set. Define an augmentedImageDatastore
object to resize the data for the network. Then, split the data into calibration and validation data sets.
unzip('MerchData.zip'); imds = imageDatastore('MerchData', ... 'IncludeSubfolders',true, ... 'LabelSource','foldernames'); [calData, valData] = splitEachLabel(imds, 0.7, 'randomized'); aug_calData = augmentedImageDatastore([227 227], calData); aug_valData = augmentedImageDatastore([227 227], valData);
At the MATLAB command prompt, open the app.
deepNetworkQuantizer
In the app, click New and select Quantize a network
.
The app verifies your execution environment. For more information, see Quantization Workflow Prerequisites.
In the dialog, select the execution environment and the network to quantize from the base workspace. For this example, select a CPU execution environment and net - DAGnetwork
.
Decide whether to use the Network Preparation option. Network preparation modifies your neural network to improve performance and avoid error conditions in the quantization workflow. These modifications include conversion of your network to a dlnetwork
object. For more information, see prepareNetwork
.
For this example, deselect the Prepare network for quantization check box so the network remains a DAGNetwork
object. This workflow uses the default metric function for validation, which is only supported for DAGNetwork
and SeriesNetwork
objects.
The app displays the layer graph of the selected network.
In the Calibrate section of the toolstrip, under Calibration Data, select the augmentedImageDatastore
object from the base workspace containing the calibration data, aug_calData
. Select Calibrate.
The Deep Network Quantizer uses the calibration data to exercise the network and collect range information for the learnable parameters in the network layers.
When the calibration is complete, the app displays a table containing the weights and biases in the convolution and fully connected layers of the network as well as the dynamic ranges of the activations in all layers of the network and their minimum and maximum values during the calibration. To the right of the table, the app displays histograms of the dynamic ranges of the parameters.
In the Quantize column of the table, indicate whether to quantize the learnable parameters in the layer. Layers that are not quantized remain in single precision after quantization.
In the Quantize section of the toolstrip, under Quantize, select the MinMax exponent scheme. Click Quantize.
The Deep Network Quantizer quantizes the weights, activations, and biases of convolution layers in the network to scaled 8-bit integer data types. The app updates the histogram of the dynamic ranges of the parameters. The gray regions of the histograms indicate data that cannot be represented by the quantized representation. For more information on how to interpret these histograms, see Quantization of Deep Neural Networks.
In the Validate section of the toolstrip, under Validation Data, select the augmentedImageDatastore
object from the base workspace containing the validation data, aug_valData
.
In the Validate section of the toolstrip, under Hardware Settings, select Raspberry Pi as the Simulation Environment. The app auto-populates the Target credentials from an existing connection or from the last successful connection. You can also use this option to create a new Raspberry Pi connection.
Click Validate. The app uses the validation data to exercise the network. The app determines a default metric function to use for the validation based on the type of network that is being quantized. For a classification network, the app uses top-1 accuracy metric.
When the validation is complete, the app displays the results of the validation, including:
Metric function used for validation
Result of the metric function before and after quantization
Memory requirement of the network before and after quantization (MB)
If the performance of the quantized network is not satisfactory, you can choose to not quantize some layers by clearing the layer in the table. You can also explore the effects of choosing a different exponent selection scheme for quantization in the Quantize drop-down list. To see the effects of these changes, quantize and validate the network again.
After calibrating and quantizing the network, you can choose to export the quantized network or the dlquantizer
object. Select the Export button. In the drop down, select from the following options:
Export Quantized Network — Add the quantized network to the base workspace. This option exports a simulatable quantized network that you can explore in MATLAB without deploying to hardware.
Export Quantizer — Add the
dlquantizer
object to the base workspace. You can save thedlquantizer
object and use it for further exploration in the Deep Network Quantizer app or at the command line, or use it to generate code for your target hardware.Generate Code — Open the MATLAB Coder app and generate C++ code from the quantized neural network. Generating C++ code requires a MATLAB Coder™ license.
Quantize Network for FPGA Deployment
To explore the behavior of a neural network that has quantized
convolution layers, use the Deep Network Quantizer app. This example
quantizes the learnable parameters of the convolution layers of the
LogoNet
neural network for an FPGA target.
For this example, you need the products listed under FPGA
in Quantization Workflow Prerequisites.
Load the pretrained network to quantize into the base workspace. Create a file in
your current working folder called getLogoNetwork.m
. In the file,
enter this code.
function net = getLogoNetwork if ~isfile('LogoNet.mat') url = 'https://www.mathworks.com/supportfiles/gpucoder/cnn_models/logo_detection/LogoNet.mat'; websave('LogoNet.mat',url); end data = load('LogoNet.mat'); net = data.convnet; end
Load the pretrained network.
snet = getLogoNetwork;
snet = SeriesNetwork with properties: Layers: [22×1 nnet.cnn.layer.Layer] InputNames: {'imageinput'} OutputNames: {'classoutput'}
Define calibration and validation data to use for quantization.
The Deep Network Quantizer app uses calibration data to exercise the network and collect the dynamic ranges of the weights and biases in the convolution and fully connected layers of the network. The app also exercises the dynamic ranges of the activations in all layers of the LogoNet network. For the best quantization results, the calibration data must be representative of inputs to the LogoNet network.
After quantization, the app uses the validation data set to test the network to understand the effects of the limited range and precision of the quantized learnable parameters of the convolution layers in the network.
In this example, use the images in the logos_dataset
data set
to calibrate and validate the LogoNet network. Define an
imageDatastore
object, then split the data into calibration and
validation data sets.
Expedite the calibration and validation process for this example by using a subset
of the calibration and validation data. Store the new reduced calibration data set
in calData_concise
and the new reduced validation data set in
valData_concise
.
currentDir = pwd; openExample('deeplearning_shared/QuantizeNetworkForFPGADeploymentExample') unzip('logos_dataset.zip'); imds = imageDatastore(fullfile(currentDir,'logos_dataset'),... 'IncludeSubfolders',true,'FileExtensions','.JPG','LabelSource','foldernames'); [calData,valData] = splitEachLabel(imds,0.7,'randomized'); calData_concise = calData.subset(1:20); valData_concise = valData.subset(1:6);
Open the Deep Network Quantizer app.
deepNetworkQuantizer
Click New and select Quantize a
network
.
Set the execution environment to FPGA and select snet -
SeriesNetwork
as the network to quantize.
Decide whether to use the Network Preparation option. Network
preparation modifies your neural network to improve performance and avoid error
conditions in the quantization workflow. These modifications include conversion of
your network to a dlnetwork
object. For more information, see
prepareNetwork
.
For this example, deselect the Prepare network for
quantization check box so the network remains a
SeriesNetwork
object. This workflow uses the default metric
function for validation, which is only supported for DAGNetwork
and
SeriesNetwork
objects.
The app displays the layer graph of the selected network.
Under Calibration Data, select the calData_concise -
ImageDatastore
object from the base workspace containing the
calibration data.
Click Calibrate. By default, the app uses the host GPU to collect calibration data, if one is available. Otherwise, the host CPU is used. You can use the Calibrate drop-down menu to select the calibration environment.
The Deep Network Quantizer app uses the calibration data to exercise the network and collect range information for the learnable parameters in the network layers.
When the calibration is complete, the app displays a table containing the weights and biases in the convolution and fully connected layers of the network. Also displayed are the dynamic ranges of the activations in all layers of the network and their minimum and maximum values recorded during the calibration. The app displays histograms of the dynamic ranges of the parameters.
In the Quantize Layer column of the table, indicate whether to quantize the learnable parameters in the layer. Layers that are not quantized remain in single precision.
From the Quantize button drop-down list, select the MinMax exponent scheme.
Click Quantize.
The Deep Network Quantizer app quantizes the weights, activations, and biases of layers in the network to scaled 8-bit integer data types. The gray regions of the histograms indicate data that cannot be represented by the quantized representation. For more information on how to interpret these histograms, see Quantization of Deep Neural Networks.
Under Validation Data, select the valData_concise -
ImageDatastore
object from the base workspace containing the validation
data.
From the Validation Options drop-down list, select the Default metric function.
In the Hardware Settings section of the toolstrip, select the environment to use for validation of the quantized network. For more information on these options, see Hardware Settings.
This example uses Xilinx ZC706 (zc706_int8) and JTAG.
Click Validate.
The Deep Network Quantizer app uses the validation data to exercise the network. The app determines a default metric function to use for the validation based on the type of network that is being quantized. For more information, see Validation Options.
When the validation is complete, the app displays the validation results.
After quantizing and validating the network, you can choose to export the quantized network.
Click the Export button. In the drop-down list, select
Export Quantizer to create a
dlquantizer
object in the base workspace. You can deploy the
quantized network to your target FPGA board and retrieve the prediction results by
using MATLAB. For an example, see Classify Images on FPGA Using Quantized Neural Network (Deep Learning HDL Toolbox).
Import dlquantizer
Object into Deep Network Quantizer App
Import a dlquantizer
object from the base workspace into the
Deep Network Quantizer app to begin quantization of a deep neural network
using either the command line or the app, and resume your work later in the app.
Open the Deep Network Quantizer app.
deepNetworkQuantizer
In the app, click New and select Import
dlquantizer object
.
In the dialog, select a dlquantizer
object to import from the
base workspace. For this example, use the dlquantizer
object
quantizer
from the above example Quantize a Neural Network
for GPU Target. You can create the quantizer
object by selecting
Export Quantizer from the Export
drop-down list after quantizing the network.
The app imports any data contained in the dlquantizer
object that
was collected at the command line, including the quantized network, calibration
data, validation data, and calibration statistics.
The app displays a table containing the quantization data contained in the
imported dlquantizer
object, quantizer
. To the
right of the table, the app displays histograms of the dynamic ranges of the
parameters. The gray regions of the histograms indicate data that cannot be
represented by the quantized representation. For more information on how to
interpret these histograms, see Quantization of Deep Neural Networks.
Related Examples
Parameters
Execution Environment
— Execution Environment
GPU
(default) | FPGA
| CPU
| MATLAB
When you select New > Quantize a Network, the app allows you to choose the execution environment for the quantized network. How the network is quantized depends on the choice of execution environment.
When you select the MATLAB
execution environment, the app
performs target-agnostic quantization of the neural network. You do not need to have the
target hardware to explore the quantized network in MATLAB.
Network Preparation
— Prepare network for quantization
on (default) | off
When you select Network Preparation, your neural network is modified to improve
performance and avoid error conditions. For more information, see prepareNetwork
.
Hardware Settings
— Hardware settings
simulation environment | target
Specify hardware settings based on your execution environment.
GPU Execution Environment
Select from the following simulation environments:
Simulation Environment Action GPU
Simulate on host GPU
Deploys the quantized network to the host GPU. Validates the quantized network by comparing performance to single-precision version of the network.
MATLAB
Simulate in MATLAB
Simulates the quantized network in MATLAB. Validates the quantized network by comparing performance to single-precision version of the network.
FPGA Execution Environment
Select from the following simulation environments:
Simulation Environment Action Cosimulation
Emulate on host
Emulates the quantized network in MATLAB. Validates the quantized network by comparing performance to single-precision version of the network. Intel Arria 10 SoC
arria10soc_int8
Deploys the quantized network to an Intel® Arria® 10 SoC board by using the
arria10soc_int8
bitstream. Validates the quantized network by comparing performance to single-precision version of the network.Xilinx ZCU102
zcu102_int8
Deploys the quantized network to a Xilinx® Zynq® UltraScale+™ MPSoC ZCU102 10 SoC board by using the
zcu102_int8
bitstream. Validates the quantized network by comparing performance to single-precision version of the network.Xilinx ZC706
zc706_int8
Deploys the quantized network to a Xilinx Zynq-7000 ZC706 board by using the
zc706_int8
bitstream. Validates the quantized network by comparing performance to single-precision version of the network.Each FPGA board selection uses a default bitstream that is specialized to meet the resource requirements of the board. If you want to specify another bitstream to use for deployment, select Add Custom Bitstream.
When you select the Intel Arria 10 SoC, Xilinx ZCU102, or Xilinx ZC706 option, additionally select the interface to use to deploy and validate the quantized network.
Target Option Action JTAG Programs the target FPGA board selected under Simulation Environment by using a JTAG cable. For more information, see JTAG Connection (Deep Learning HDL Toolbox). Ethernet Programs the target FPGA board selected in Simulation Environment through the Ethernet interface. Specify the IP address for your target board in the IP Address field. CPU Execution Environment
Select from the following simulation environments:
Simulation Environment Action Raspberry Pi
Deploys the quantized network to the Raspberry Pi board. Validates the quantized network by comparing performance to single-precision version of the network.
When you select the Raspberry Pi option, additionally specify the following details for the
raspi
connection.Target Option Description Hostname Hostname of the board, specified as a string. Username Linux® username, specified as a string. Password Linux user password, specified as a string.
Exponent Scheme
— Exponent scheme for quantization
MinMax
(default) | Histogram
Select the exponent selection scheme to use for quantization of the network:
MinMax — Evaluate the exponent based on the range information in the calibration statistics and avoid overflows.
Histogram — Distribution-based scaling which evaluates the exponent to best fit the calibration data.
Validation Options
— Options for validation
metric function
The Deep Network Quantizer app determines a metric function to use for the validation based on the type of the quantized network.
The default metric functions are supported for DAGNetwork
and
SeriesNetwork
objects. You must define a custom metric function for a
dlnetwork
object. For an example of a custom metric function for a
dlnetwork
object, see Quantize Multiple-Input Network Using Image and Feature Data.
Type of Network | Default Metric Function |
---|---|
Classification | Top-1 Accuracy — Accuracy of the network |
Object detection | Average Precision — Average
precision over all detection results. See |
Regression | MSE — Mean squared error of the network |
Semantic segmentation | WeightedIOU — Average IoU of each
class, weighted by the number of pixels in that class. See |
Export
— Options for exporting quantized network
Export Quantized Network
| Export Quantizer
| Generate Code
Export Quantized Network — After calibrating the network, quantize and add the quantized network to the base workspace. This option exports a simulatable quantized network,
quantizedNet
, that you can explore in MATLAB without deploying to hardware. This option is equivalent to usingquantize
at the command line.Code generation is not supported for the exported quantized network.
Export Quantizer — Add the
dlquantizer
object to the base workspace. You can save thedlquantizer
object and use it for further exploration in the Deep Network Quantizer app or at the command line, or use it to generate code for your target hardware.Generate Code
Execution Environment Code Generation GPU Open the GPU Coder app and generate GPU code from the quantized and validated neural network. Generating GPU code requires a GPU Coder license. CPU Open the MATLAB Coder app and generate C++ code from the quantized and validated neural network. Generating C++ code requires a MATLAB Coder license.
Limitations
Validation on target hardware for CPU, FPGA, and GPU execution environments is not supported in MATLAB Online™. FPGA and GPU execution environments, perform validation through emulation on the MATLAB Online host. You can also perform GPU validation if GPU support has been added to your MATLAB Online Server™ cluster. For more information on GPU support for MATLAB Online, see Configure GPU Support in MATLAB Online Server (MATLAB Online Server).
Version History
Introduced in R2020aR2024b: Fixed-Point Designer license required for Execution Environment MATLAB
When you use the MATLAB Execution Environment for quantization, simulation of the network is performed using fixed-point data types. This simulation requires a Fixed-Point Designer™ license for the quantization and validation steps of the deep learning quantization workflow.
R2023a: Specify Raspberry Pi connection in Hardware Settings
You can now validate the quantized network on a Raspberry Pi® board by selecting Raspberry Pi in
Hardware Settings
and specifying target credentials for a
raspi
object in the validation step.
R2022b: Calibrate on host GPU or host CPU
You can now choose whether to calibrate your network using the host GPU or host CPU. By
default, the calibrate
function and the Deep Network
Quantizer app will calibrate on the host GPU if one is available.
In previous versions, it was required that the execution environment be the same as the instrumentation environment used for the calibration step of quantization.
R2022b: dlnetwork
support
The Deep Network Quantizer app now supports calibration and validation for
dlnetwork
objects.
R2022a: Validate the performance of quantized network for CPU target
The Deep Network Quantizer app now supports the quantization and validation workflow for CPU targets.
R2022a: Quantize neural networks without a specific target
Specify MATLAB
as the Execution Environment
to
quantize your neural networks without generating code or committing to a specific target for
code deployment. This can be useful if you:
Do not have access to your target hardware.
Want to inspect your quantized network without generating code.
Your quantized network implements int8
data instead of
single
data. It keeps the same layers and connections as the original
network, and it has the same inference behavior as it would when running on hardware.
Once you have quantized your network, you can use the
quantizationDetails
function to inspect your quantized network.
Additionally, you also have the option to deploy the code to a GPU target.
See Also
Functions
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