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SOTA
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Accelerator Toolkit
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Deep Learning Toolkit
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- Resume
- Add
- AlphaDropout
- AdditiveAttention
- Attention
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- AvgPool1D
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- Conv1D
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- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
- ELU
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- GaussianDropout
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- GlobalAvgPool1D
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- GlobalAvgPool3D
- GlobalMaxPool1D
- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- GELU
- Input
- LayerNormalization
- LSTM
- MaxPool1D
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- MultiHeadAttention
- HardSigmoid
- LeakyReLU
- Linear
- Multiply
- Permute3D
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- RNN
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- SELU
- Output Predict
- Output Train
- SeparableConv1D
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- SimpleRNN
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- Exp
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- AveragePool
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- Celu
- ConcatFromSequence
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- DynamicTimeWarping
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- EyeLike
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- GlobalAveragePool
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- HammingWindow
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- lrfft
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- Log
- LogSoftmax
- LpNormalization
- LpPool
- LRN
- MeanVarianceNormalization
- MicrosoftGelu
- Mish
- Multinomial
- MurmurHash3
- Neg
- NhwcMaxPool
- NonZero
- Not
- OptionalGetElement
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- QuickGelu
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- ReduceSumInteger
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- Shape
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- Sin
- Sign
- Sinh
- Size
- SpaceToDepth
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- StringNormalizer
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- TfldfVectorizer
- Tokenizer
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- UnfoldTensor
- lslnf
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- Add
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- ConvInteger
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- CropAndResize
- CumSum
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- DFT
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- GatherElements
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- Gemm
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- GreaterOrEqual
- GreedySearch
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- Less
- LessOrEqual
- LongformerAttention
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- MaxPoolWithMask
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- QLinearWhere
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- Range
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- ReduceProd
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- RelativePositionBias
- Reshape
- Resize
- RestorePadding
- ReverseSequence
- RoiAlign
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- SequenceAt
- SequenceErase
- SequenceInsert
- Sinh
- Slice
- SparseToDenseMatMul
- SplitToSequence
- Squeeze
- STFT
- StringConcat
- Sub
- Tile
- TorchEmbedding
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- Where
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- Attention
- AttnLSTM
- BatchNormalization
- BiasDropout
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- Dropout
- DynamicQuantizeLinear
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- GRU
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- MicrosoftMultiHeadAttention
- QAttention
- RemovePadding
- RNN
- Sampling
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- SkipSimplifiedLayerNormalization
- SoftmaxCrossEntropyLoss
- SparseAttention
- TopK
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- AdditiveAttention
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- Conv1D
- Conv2D
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- Conv2DTranspose
- Conv3DTranspose
- Conv3D
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- DepthwiseConv2D
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- LayerNormalization
- GRU
- LSTM
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- PReLU 3D
- PReLU 4D
- PReLU 5D
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- SeparableConv2D
- MultiHeadAttention
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- RNN (LSTM)
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- SimpleRNN
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- 2D
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- 4D
- 5D
- 6D
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- AdditiveAttention
- Attention
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- Conv2D
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- Conv2DTranspose
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- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv3DTranspose
- DepthwiseConv2D
- Dense
- Embedding
- LayerNormalization
- GRU
- PReLU 2D
- PReLU 3D
- PReLU 4D
- MultiHeadAttention
- LSTM
- PReLU 5D
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
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- 3D
- 4D
- 5D
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- AdditiveAttention
- Attention
- BatchNormalization
- Bidirectional
- Conv1D
- Conv2D
- Conv3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Dense
- DepthwiseConv2D
- Embedding
- GRU
- LayerNormalization
- LSTM
- MultiHeadAttention
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Resume
- SeparableConv1D
- SeparableConv2D
- SimpleRNN
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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
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- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
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- SimpleRNN
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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
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- Accuracy
- BinaryAccuracy
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- CategoricalAccuracy
- CategoricalCrossentropy
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- CosineSimilarity
- FalseNegatives
- FalsePositives
- Hinge
- Huber
- IoU
- KLDivergence
- LogCoshError
- Mean
- MeanAbsoluteError
- MeanAbsolutePercentageError
- MeanIoU
- MeanRelativeError
- MeanSquaredError
- MeanSquaredLogarithmicError
- MeanTensor
- OneHotIoU
- OneHotMeanIoU
- Poisson
- Precision
- PrecisionAtRecall
- Recall
- RecallAtPrecision
- RootMeanSquaredError
- SensitivityAtSpecificity
- SparseCategoricalAccuracy
- SparseCategoricalCrossentropy
- SparseTopKCategoricalAccuracy
- Specificity
- SpecificityAtSensitivity
- SquaredHinge
- Sum
- TopKCategoricalAccuracy
- TrueNegatives
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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
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- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
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- GRU
- LSTM
- RNN (GRU)
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- Dense
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- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
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- Conv3D
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- ConvLSTM2D
- ConvLSTM3D
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- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
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Computer Vision Toolkit
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CUDA Toolkit
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- Resume
- Array size
- Index Array
- Replace Subset
- Insert Into Array
- Delete From Array
- Initialize Array
- Build Array
- Concatenate Array
- Array Subset
- Min & Max
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- Short Array
- Reverse 1D array
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- Search In Array
- Split 1D Array
- Split 2D Array
- Rotate 1D Array
- Increment Array Element
- Decrement Array Element
- Interpolate 1D Array
- Threshold 1D Array
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- Remove Duplicate From 1D Array
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- Add Array Element
- Multiply Array Element
- Absolute
- Round To Nearest
- Round Toward -Infinity
- Round Toward +Infinity
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Getting Started
Install the Online Accelerator Toolkit
- Access LabVIEW Accelerator Module: In the SOTA interface, locate and click on the LabVIEW Accelerator module.
- Select Version and Install: Select your LabVIEW version and click on the install button.
- Accept License Agreement: Accept the terms of the license agreement to install the toolkit.
- Launch LabVIEW: Once the installation is complete, you can launch LabVIEW and start utilizing the LabVIEW Accelerator toolkit.
Congratulations! You have successfully installed the Accelerator toolkit using SOTA, and now you can make the most of your Accelerator toolkit within LabVIEW.
Note: Installing the LabVIEW Accelerator Toolkit requires SOTA to be installed first. SOTA provides the interface to select, install, and activate the LabVIEW Accelerator Toolkit within your LabVIEW environment.
If you encounter any issues or have any questions during the installation process, please refer reach out to our support team for assistance on support community page.
We appreciate your interest in the LabVIEW Accelerator toolkit and hope you find this installation guide helpful.
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Install the Local Accelerator Toolkit
- Access LabVIEW Accelerator Module: You can download the Accelerator module by visiting the following link: https://graiphic.io/download-version/
- Select Version and Install: Select your LabVIEW version and click on the install button.
- Accept License Agreement: Accept the terms of the license agreement to install the toolkit.
- Launch LabVIEW: Once the installation is complete, you can launch LabVIEW and start utilizing the LabVIEW Accelerator toolkit.
Congratulations! You have successfully installed the Accelerator toolkit using SOTA, and now you can make the most of your Accelerator toolkit within LabVIEW.
Note: Installing the LabVIEW Accelerator Toolkit requires SOTA to be installed first. SOTA provides the interface to select, install, and activate the LabVIEW Accelerator Toolkit within your LabVIEW environment.
If you encounter any issues or have any questions during the installation process, please refer reach out to our support team for assistance on support community page.
We appreciate your interest in the LabVIEW Accelerator toolkit and hope you find this installation guide helpful.
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System Requirements
Technical support
The support is managed via the support community page. You can post all your questions, thoughts or suggestions about LabVIEW Accelerator toolkit and other Graiphic product.
Releases notes
The LabVIEW Accelerator toolkit toolkit is constantly updated. Latest release note is available HERE.

