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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
- Average
- AvgPool1D
- AvgPool2D
- AvgPool3D
- BatchNormalization
- Bidirectional
- Concatenate
- Conv1D
- Conv1DTranspose
- Conv2D
- Conv2DTranspose
- Conv3D
- Conv3DTranspose
- ConvLSTM1D
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- ConvLSTM3D
- Dense
- Cropping1D
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- Cropping3D
- DepthwiseConv2D
- Dropout
- Embedding
- Flatten
- ELU
- Exponential
- GaussianDropout
- GaussianNoise
- GlobalAvgPool1D
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- GlobalAvgPool3D
- GlobalMaxPool1D
- GlobalMaxPool2D
- GlobalMaxPool3D
- GRU
- GELU
- Input
- LayerNormalization
- LSTM
- MaxPool1D
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- MaxPool3D
- MultiHeadAttention
- HardSigmoid
- LeakyReLU
- Linear
- Multiply
- Permute3D
- Reshape
- RNN
- PReLU
- ReLU
- SELU
- Output Predict
- Output Train
- SeparableConv1D
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- SimpleRNN
- SpatialDropout
- Sigmoid
- SoftMax
- SoftPlus
- SoftSign
- Split
- UpSampling1D
- UpSampling2D
- UpSampling3D
- ZeroPadding1D
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- ZeroPadding3D
- Swish
- TanH
- ThresholdedReLU
- Substract
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- Exp
- Identity
- Abs
- Acos
- Acosh
- ArgMax
- ArgMin
- Asin
- Asinh
- Atan
- Atanh
- AveragePool
- Bernouilli
- BitwiseNot
- BlackmanWindow
- Cast
- Ceil
- Celu
- ConcatFromSequence
- Cos
- Cosh
- DepthToSpace
- Det
- DynamicTimeWarping
- Erf
- EyeLike
- Flatten
- Floor
- GlobalAveragePool
- GlobalLpPool
- GlobalMaxPool
- HammingWindow
- HannWindow
- HardSwish
- HardMax
- lrfft
- lsNaN
- Log
- LogSoftmax
- LpNormalization
- LpPool
- LRN
- MeanVarianceNormalization
- MicrosoftGelu
- Mish
- Multinomial
- MurmurHash3
- Neg
- NhwcMaxPool
- NonZero
- Not
- OptionalGetElement
- OptionalHasElement
- QuickGelu
- RandomNormalLike
- RandomUniformLike
- RawConstantOfShape
- Reciprocal
- ReduceSumInteger
- RegexFullMatch
- Rfft
- Round
- SampleOp
- Shape
- SequenceLength
- Shrink
- Sin
- Sign
- Sinh
- Size
- SpaceToDepth
- Sqrt
- StringNormalizer
- Tan
- TfldfVectorizer
- Tokenizer
- Transpose
- UnfoldTensor
- lslnf
- ImageDecoder
- Inverse
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- Add
- AffineGrid
- And
- BiasAdd
- BiasGelu
- BiasSoftmax
- BiasSplitGelu
- BitShift
- BitwiseAnd
- BitwiseOr
- BitwiseXor
- CastLike
- CDist
- CenterCropPad
- Clip
- Col2lm
- ComplexMul
- ComplexMulConj
- Compress
- ConvInteger
- Conv
- ConvTranspose
- ConvTransposeWithDynamicPads
- CropAndResize
- CumSum
- DeformConv
- DequantizeBFP
- DequantizeLinear
- DequantizeWithOrder
- DFT
- Div
- DynamicQuantizeMatMul
- Equal
- Expand
- ExpandDims
- FastGelu
- FusedConv
- FusedGemm
- FusedMatMul
- FusedMatMulActivation
- GatedRelativePositionBias
- Gather
- GatherElements
- GatherND
- Gemm
- GemmFastGelu
- GemmFloat8
- Greater
- GreaterOrEqual
- GreedySearch
- GridSample
- GroupNorm
- InstanceNormalization
- Less
- LessOrEqual
- LongformerAttention
- MatMul
- MatMulBnb4
- MatMulFpQ4
- MatMulInteger
- MatMulInteger16
- MatMulIntergerToFloat
- MatMulNBits
- MaxPoolWithMask
- MaxRoiPool
- MaxUnPool
- MelWeightMatrix
- MicrosoftDequantizeLinear
- MicrosoftGatherND
- MicrosoftGridSample
- MicrosoftPad
- MicrosoftQLinearConv
- MicrosoftQuantizeLinear
- MicrosoftRange
- MicrosoftTrilu
- Mod
- MoE
- Mul
- MulInteger
- NegativeLogLikelihoodLoss
- NGramRepeatBlock
- NhwcConv
- NhwcFusedConv
- NonMaxSuppression
- OneHot
- Or
- PackedAttention
- PackedMultiHeadAttention
- Pad
- Pow
- QGemm
- QLinearAdd
- QLinearAveragePool
- QLinearConcat
- QLinearConv
- QLinearGlobalAveragePool
- QLinearLeakyRelu
- QLinearMatMul
- QLinearMul
- QLinearReduceMean
- QLinearSigmoid
- QLinearSoftmax
- QLinearWhere
- QMoE
- QOrderedAttention
- QOrderedGelu
- QOrderedLayerNormalization
- QOrderedLongformerAttention
- QOrderedMatMul
- QuantizeLinear
- QuantizeWithOrder
- Range
- ReduceL1
- ReduceL2
- ReduceLogSum
- ReduceLogSumExp
- ReduceMax
- ReduceMean
- ReduceMin
- ReduceProd
- ReduceSum
- ReduceSumSquare
- RelativePositionBias
- Reshape
- Resize
- RestorePadding
- ReverseSequence
- RoiAlign
- RotaryEmbedding
- ScatterElements
- ScatterND
- SequenceAt
- SequenceErase
- SequenceInsert
- Sinh
- Slice
- SparseToDenseMatMul
- SplitToSequence
- Squeeze
- STFT
- StringConcat
- Sub
- Tile
- TorchEmbedding
- TransposeMatMul
- Trilu
- Unsqueeze
- Where
- WordConvEmbedding
- Xor
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- Attention
- AttnLSTM
- BatchNormalization
- BiasDropout
- BifurcationDetector
- BitmaskBiasDropout
- BitmaskDropout
- DecoderAttention
- DecoderMaskedMultiHeadAttention
- DecoderMaskedSelfAttention
- Dropout
- DynamicQuantizeLinear
- DynamicQuantizeLSTM
- EmbedLayerNormalization
- GemmaRotaryEmbedding
- GroupQueryAttention
- GRU
- LayerNormalization
- LSTM
- MicrosoftMultiHeadAttention
- QAttention
- RemovePadding
- RNN
- Sampling
- SkipGroupNorm
- SkipLayerNormalization
- SkipSimplifiedLayerNormalization
- SoftmaxCrossEntropyLoss
- SparseAttention
- TopK
- WhisperBeamSearch
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- AdditiveAttention
- Attention
- BatchNormalization
- Bidirectional
- Conv1D
- Conv2D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Dense
- DepthwiseConv2D
- Embedding
- LayerNormalization
- GRU
- LSTM
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- MutiHeadAttention
- SeparableConv1D
- SeparableConv2D
- MultiHeadAttention
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
- 1D
- 2D
- 3D
- 4D
- 5D
- 6D
- Scalar
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- AdditiveAttention
- Attention
- BatchNormalization
- Conv1D
- Conv2D
- Conv1DTranspose
- Conv2DTranspose
- Bidirectional
- 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)
- 1D
- 2D
- 3D
- 4D
- 5D
- 6D
- Scalar
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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
- 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
- PReLU 4D
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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
- BinaryCrossentropy
- BinaryIoU
- CategoricalAccuracy
- CategoricalCrossentropy
- CategoricalHinge
- 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
- TruePositives
- Resume
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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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- Dense
- Embedding
- AdditiveAttention
- Attention
- MultiHeadAttention
- Conv1D
- Conv2D
- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
- LSTM
- RNN (GRU)
- RNN (LSTM)
- RNN (SimpleRNN)
- SimpleRNN
- BatchNormalization
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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
- Reshape Array
- Short Array
- Reverse 1D array
- Shuffle array
- Search In Array
- Split 1D Array
- Split 2D Array
- Rotate 1D Array
- Increment Array Element
- Decrement Array Element
- Interpolate 1D Array
- Threshold 1D Array
- Interleave 1D Array
- Decimate 1D Array
- Transpose Array
- Remove Duplicate From 1D Array
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- Resume
- Add
- Substract
- Multiply
- Divide
- Quotient & Remainder
- Increment
- Decrement
- Add Array Element
- Multiply Array Element
- Absolute
- Round To Nearest
- Round Toward -Infinity
- Round Toward +Infinity
- Scale By Power Of Two
- Square Root
- Square
- Negate
- Reciprocal
- Sign
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6D
Description
Adds to the ptr input array a 6D array (of type BOOL,Β SGL,Β INT,Β UINT, orΒ STRING) at a specified index. Type : polymorphic.

Input parameters
Β 6D Input Data : array,Β 6D array of data with any type : integers (signed/unsigned), floats, doubles, booleans, or strings.
Β Data in :Β array,
Β input_order :Β integer,Β defines the position of the output within the data array. It corresponds to the index assigned to the output when it is created (via the index parameter).
Β Inputs Info :Β cluster
Β inputs_ptr :Β integer,Β represents a pre-allocated device memory address (for example, a CUDA device pointer) where the input tensor data is already stored.
Β inputs_shapes :Β array,Β specifies the shape of the output tensor. Since the data is written into a pre-allocated device buffer, this shape allows the runtime to interpret the memory layout correctly.
Β inputs_ranks :Β integer,Β indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
Β inputs_types :Β enum,Β defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).
Β input order : integer,Β defines the position of the input within the data array. It corresponds to the index assigned to the input when it is created (via theΒ indexΒ parameter).
Output parameters
Β Data in :Β array,
Β input_order :Β integer,Β defines the position of the output within the data array. It corresponds to the index assigned to the output when it is created (via the index parameter).
Β Inputs Info :Β cluster
Β inputs_ptr :Β integer,Β represents a pre-allocated device memory address (for example, a CUDA device pointer) where the input tensor data is already stored.
Β inputs_shapes :Β array,Β specifies the shape of the output tensor. Since the data is written into a pre-allocated device buffer, this shape allows the runtime to interpret the memory layout correctly.
Β inputs_ranks :Β integer,Β indicates the rank of the tensor, i.e. the number of dimensions (Scalar = 0, 1D = 1, 2D = 2, etc.).
Β inputs_types :Β enum,Β defines the ONNX tensor type as an enumerated value (e.g. FLOAT, INT64, STRING).
Example
All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Accelerator library to run it).

