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No level
Level 8K
ARGMAX
Input tensor
Axis in range from 0 to rank(shape1) - 1
Output tensor, with rank = rank(shape1) - 1
AVG_POOL2D
Input tensor
[kernel_y, kernel_x]
[stride_y, stride_x]
[pad_top, pad_bottom, pad_left, pad_right]
Enumerated type, must be one of INT32, FP16, FP32, as defined in the Supported Data Types table for this operation
Input tensor zero point. Must be zero for non-int8 types.
Output tensor zero point. Must be zero for non-int8 types.
Output tensor 4D
CONV2D
Input tensor
Weight kernel size KH x KW
Per output channel bias data. +
Bias data will be broadcast if BC == 1.
[pad_top, pad_bottom, pad_left, pad_right]
[stride_y, stride_x]
[dilation_y, dilation_x]
Enumerated type, must be one of INT32, INT48, FP16, FP32, as defined in the Supported Data Types table for this operation
Input tensor zero point. Must be zero for non-int8 types.
Weight zero point. Must be zero for non-int8 types.
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
Output tensor
CONV3D
Input tensor
Weight kernel size KDxKHxKW
Per output channel bias data. +
Bias data will be broadcast if BC == 1.
[pad_d0, pad_d1, pad_top, pad_bottom, pad_left, pad_right]
[stride_d, stride_y, stride_x]
[dilation_d, dilation_y, dilation_x]
Enumerated type, must be one of INT32, INT48, FP16, FP32, as defined in the Supported Data Types table for this operation
Input tensor zero point. Must be zero for non-int8 types.
Weight zero point. Must be zero for non-int8 types.
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
Output tensor
DEPTHWISE_CONV2D
Input tensor
Weight kernel size KH x KW
Per output channel bias data. +
Bias data will be broadcast if BC == 1.
[pad_top, pad_bottom, pad_left, pad_right]
[stride_y, stride_x]
[dilation_y, dilation_x]
Enumerated type, must be one of INT32, INT48, FP16, FP32, as defined in the Supported Data Types table for this operation
Input tensor zero point. Must be zero for non-int8 types.
Weight zero point. Must be zero for non-int8 types.
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
Output tensor
FFT2D
Real part of the complex input. H,W must be powers of two.
Imaginary part of the complex input. H,W must be powers of two.
false for forward FFT, true for inverse FFT
Real part of the complex output.
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
Imaginary part of the complex output.
FULLY_CONNECTED
Input tensor
Weights
Per output channel bias data. +
Bias data will be broadcast if BC == 1.
Input tensor zero point. Must be zero for non-int8 types.
Weight zero point. Must be zero for non-int8 types.
Output tensor
MATMUL
Input tensor A, N matrices of size HxC
Input tensor B, N matrices of size CxW
Input tensor A zero point. Must be zero for non-int8 types.
Input tensor B zero point. Must be zero for non-int8 types.
Output tensor, N matrices of size HxW
MAX_POOL2D
Input tensor 4D
[kernel_y, kernel_x]
[stride_y, stride_x]
[pad_top, pad_bottom, pad_left, pad_right]
Output tensor 4D
RFFT2D
Real input. H,W must be powers of two.
Real part of the complex output
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
Imaginary part of the complex output.
TRANSPOSE_CONV2D
Input tensor
Weight kernel size KH x KW
Per output channel bias data. +
Bias data will be broadcast if BC == 1.
[out_pad_top, out_pad_bottom, out_pad_left, out_pad_right]
[stride_y, stride_x]
Enumerated type, must be one of INT32, INT48, FP16, FP32, as defined in the Supported Data Types table for this operation
Input tensor zero point. Must be zero for non-int8 types.
Weight zero point. Must be zero for non-int8 types.
This optional attribute affects the floating-point compliance error bound.
The default of false allows for direct and transform based, fast convolution algorithms.
Only set to true if direct dot-product calculation precision is required.
Output tensor
CLAMP
Input tensor
Minimum clip value
Maximum clip value
Output tensor of same type and shape as input
ERF
Input tensor
Output tensor of same type and shape as input
SIGMOID
Input tensor
Output tensor of same type and shape as input
TANH
Input tensor
Output tensor of same type and shape as input
ADD
Input tensor
Input tensor with the same rank as input1
Output tensor
ARITHMETIC_RIGHT_SHIFT
Input tensor
Input tensor with the same rank as input1
If true then the shift is rounded
Output tensor
BITWISE_AND
Input tensor
Input tensor with the same rank as input1
Output tensor
BITWISE_OR
Input tensor
Input tensor with the same rank as input1
Output tensor
BITWISE_XOR
Input tensor
Input tensor with the same rank as input1
Output tensor
INTDIV
Input tensor
Input tensor with the same rank as input1
Output tensor
LOGICAL_AND
Input tensor
Input tensor with the same rank as input1
Output tensor
LOGICAL_LEFT_SHIFT
Input tensor
Input tensor with the same rank as input1
Output tensor
LOGICAL_RIGHT_SHIFT
Input tensor
Input tensor with the same rank as input1
Output tensor
LOGICAL_OR
Input tensor
Input tensor with the same rank as input1
Output tensor
LOGICAL_XOR
Input tensor
Input tensor with the same rank as input1
Output tensor
MAXIMUM
Input tensor
Input tensor with the same rank as input1
Output tensor
MINIMUM
Input tensor
Input tensor with the same rank as input1
Output tensor
MUL
Input tensor
Input tensor with the same rank as input1
Result right shift (i32_t data type only)
Output tensor
POW
Input tensor
Input tensor with the same rank as input1
Output tensor
SUB
Input tensor
Input tensor with the same rank as input1
Output tensor
TABLE
Input tensor
Lookup table tensor
Output tensor
ABS
Input tensor
Output tensor of same type, size as the input tensor
BITWISE_NOT
Input tensor
Output tensor of same type, size as the input tensor
CEIL
Input tensor
Output tensor of same type, size as the input tensor
CLZ
Input tensor
Output tensor of same type, size as the input tensor
COS
Input tensor
Output tensor of same type and shape as input
EXP
Input tensor
Output tensor of same type, size as the input tensor
FLOOR
Input tensor
Output tensor of same type, size as the input tensor
LOG
Input tensor
Output tensor of same type, size as the input tensor
LOGICAL_NOT
Input tensor
Output tensor of same type, size as the input tensor
NEGATE
Input tensor
Input 1 zero point. Must be zero for non-int8 types.
Output zero point. Must be zero for non-int8 types.
Output tensor of same type, size as the input tensor
RECIPROCAL
Input tensor
Output tensor of same type, size as the input tensor
RSQRT
Input tensor
Output tensor of same type, size as the input tensor
SELECT
Input selector tensor
Input value tensor if input1 is True
Input value tensor if input1 is False
Output tensor of same type as input2 and input3
SIN
Input tensor
Output tensor of same type and shape as input
EQUAL
Input tensor
Input tensor with the same rank as input1
Output tensor
GREATER
Input tensor
Input tensor with the same rank as input1
Output tensor
GREATER_EQUAL
Input tensor
Input tensor with the same rank as input1
Output tensor
REDUCE_ALL
Input tensor
Axis to reduce, in range from 0 to rank(shape1)-1
Output tensor. Same rank as the input tensor.
REDUCE_ANY
Input tensor
Axis to reduce, in range from 0 to rank(shape1)-1
Output tensor. Same rank as the input tensor.
REDUCE_MAX
Input tensor
Axis to reduce, in range from 0 to rank(shape1)-1
Output tensor. Same rank as the input tensor.
REDUCE_MIN
Input tensor
Axis to reduce, in range from 0 to rank(shape1)-1
Output tensor. Same rank as the input tensor.
REDUCE_PRODUCT
Input tensor
Axis to reduce, in range from 0 to rank(shape1)-1
Output tensor. Same rank as the input tensor.
REDUCE_SUM
Input tensor
Axis to reduce, in range from 0 to rank(shape1)-1
Output tensor. Same rank as the input tensor.
CONCAT
List of input tensors. All inputs must have the same rank and data type
Axis along which concatenation is to occur, in range from 0 to rank(shape)-1
Output tensor
PAD
Input tensor
Number of pad elements at the start and end of each dimension.
The values in padding are interpreted as start, end of each dimension.
As an example for rank 2, the values would be interpreted as [start_dim0, end_dim0, start_dim1, end_dim1].
Constant value to be used as padding
Output tensor of same type as the input tensor
RESHAPE
Input tensor
shape_t giving the new shape.
Output tensor of same type, size as the input tensor
REVERSE
Input tensor
Axis to reverse, in range from 0 to rank(shape)-1
Output tensor. Same shape as input tensor
SLICE
Input tensor
List of integer coordinates, of length equal to the rank of input1. Start coordinate for slicing.
List of integer size values, of length equal to the rank of input1. Size of the input to be
used.
Output tensor of same type as the input tensor
TILE
Input tensor
Number of times to replicate input1 in each dimension
Output tensor of same type, rank as the input tensor
TRANSPOSE
Input tensor
List of integers of length equal to the rank of input1. Values must be valid dimensions within shape1, and may not be repeated.
Output tensor of same type, rank as the input tensor
GATHER
3D value tensor
2D index tensor
3D output tensor
SCATTER
3D values in tensor
2D index tensor
3D input tensor
3D output tensor
RESIZE
Input tensor
[scale_y_n, scale_y_d, scale_x_n, scale_x_d]
[offset_y, offset_x]
[border_y, border_x]
BILINEAR or NEAREST
Output tensor
CAST
Input tensor
Output tensor
RESCALE
Input tensor
Output tensor with the same shape as input
Input tensor zero point. int8/uint8 can have zero point within their valid range. uint16 zero point must be either 0 or 32768. All other types must have zero point equal to 0.
Output tensor zero point.int8/uint8 can have zero point within their valid range. uint16 zero point must be either 0 or 32768. All other types must have zero point equal to 0.
Scaling multiplier array
Scaling shift array
if (scale32) mul_t=i32_t else mul_t=i16_t
Select double round mode
if (per_channel) NC=shape[rank(shape)-1] else NC=1
If True, treat the input values as unsigned.
If True, treat the output values as unsigned.
CONST
Constant values
Output tensor
IDENTITY
Input tensor
Output tensor of the same type, size as the input tensor
CUSTOM
List of input tensors
String which tells the backend which custom operator is being called
String idenifier which can help avoid name collisions on the operator field.
Different implementations of a given operator would be in different domains.
Implementations can choose which domains they want to support.
String value containing implementation specific attributes which apply to the operation
List of output tensors
COND_IF
Input condition as a size 1 tensor
List of input tensors
TOSA graph to execute if condition is true
TOSA graph to execute if condition is false
List of output tensors
WHILE_LOOP
List of input tensors
TOSA graph to evaluate the condition
TOSA graph to execute the loop body
List of output tensors
VARIABLE
Globally unique identifier for the declared variable tensor.
The variable tensor shape
Type of the tensor variable elements.
Initial value of the variable tensor. This argument is optional with default value NULL.
VARIABLE_WRITE
Globally unique identifier of the variable tensor that is writing to
Input tensor
VARIABLE_READ
Globally unique identifier of the variable tensor that is reading from
Output tensor
ADD_SHAPE
Input 1
Input 2
Output shape
CONCAT_SHAPE
List of input shape values
Output shape
CONST_SHAPE
Constant shape
Output shape
DIM
Input tensor
Axis in range from 0 to rank(shape) - 1
Output shape type of size 1 giving the size of the shape for the given axis
DIV_SHAPE
Input 1
Input 2
Output shape
MUL_SHAPE
Input 1
Input 2
Output shape
SUB_SHAPE
Input 1
Input 2
Output shape