vela: Add SUPPORTED_OPS.md generated file

This file is generated from the vela option
--supported-ops-report

Each release, a snapshot will be taken and uploaded with the release.
This is for the 2.0.0 release

Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: I6b618889758a1a078e21244f1f98a56800a528a3

2 files changed, 290 insertions, 0 deletions

diff --git a/README.md b/README.md
index cdc065a..2e3d613 100644
--- a/README.md
+++ b/README.md
@@ -177,6 +177,11 @@ Some example networks that contain quantised operators which can be compiled by
 Vela to run on the Ethos-U NPU can be found at:
 <https://tfhub.dev/s?deployment-format=lite&q=quantized>
 
+## Supported Operators
+
+Please see [Supported Operators](SUPPORTED_OPS.md) for the list of supported
+operators in this release.
+
 ## Testing
 
 Please see [Vela Testing](TESTING.md).
diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
new file mode 100644
index 0000000..81a3bbc
--- /dev/null
+++ b/SUPPORTED_OPS.md
@@ -0,0 +1,285 @@
+# Supported Ops
+
+This file was automatically generated by Vela using the `--supported-ops-report` parameter.  
+Vela version: `2.0.0`
+
+This file complies with [**CommonMark.**](https://commonmark.org)
+
+## Summary Table
+
+The table below contains TFLite operators that can be placed on the Ethos-U NPU.  
+If the constraints are not met, then that operator will be scheduled on the CPU instead.  
+For any other TFLite operator not listed, will be left untouched and scheduled on the CPU.  
+Please check the supported operator list for your chosen runtime for further information.
+
+| Operator | Constraints |
+| - | - |
+| ABS | [Generic](#generic-constraints), [Specific](#abs-constraints) |
+| ADD | [Generic](#generic-constraints), [Specific](#add-constraints) |
+| AVERAGE_POOL_2D | [Generic](#generic-constraints), [Specific](#average_pool_2d-constraints) |
+| CONCATENATION | [Generic](#generic-constraints), [Specific](#concatenation-constraints) |
+| CONV_2D | [Generic](#generic-constraints), [Specific](#conv_2d-constraints) |
+| DEPTHWISE_CONV_2D | [Generic](#generic-constraints), [Specific](#depthwise_conv_2d-constraints) |
+| FULLY_CONNECTED | [Generic](#generic-constraints), [Specific](#fully_connected-constraints) |
+| LEAKY_RELU | [Generic](#generic-constraints), [Specific](#leaky_relu-constraints) |
+| LOGISTIC | [Generic](#generic-constraints) |
+| MAXIMUM | [Generic](#generic-constraints), [Specific](#maximum-constraints) |
+| MAX_POOL_2D | [Generic](#generic-constraints), [Specific](#max_pool_2d-constraints) |
+| MINIMUM | [Generic](#generic-constraints), [Specific](#minimum-constraints) |
+| MUL | [Generic](#generic-constraints), [Specific](#mul-constraints) |
+| PACK | [Generic](#generic-constraints) |
+| QUANTIZE | [Generic](#generic-constraints) |
+| RELU | [Generic](#generic-constraints), [Specific](#relu-constraints) |
+| RELU6 | [Generic](#generic-constraints), [Specific](#relu6-constraints) |
+| RELU_N1_TO_1 | [Generic](#generic-constraints), [Specific](#relu_n1_to_1-constraints) |
+| RESHAPE | [Generic](#generic-constraints) |
+| RESIZE_BILINEAR | [Generic](#generic-constraints), [Specific](#resize_bilinear-constraints) |
+| SLICE | [Generic](#generic-constraints) |
+| SOFTMAX | [Generic](#generic-constraints), [Specific](#softmax-constraints) |
+| SPLIT | [Generic](#generic-constraints) |
+| SPLIT_V | [Generic](#generic-constraints), [Specific](#split_v-constraints) |
+| SQUEEZE | [Generic](#generic-constraints) |
+| STRIDED_SLICE | [Generic](#generic-constraints), [Specific](#strided_slice-constraints) |
+| SUB | [Generic](#generic-constraints), [Specific](#sub-constraints) |
+| TANH | [Generic](#generic-constraints) |
+| TRANSPOSE_CONV | [Generic](#generic-constraints), [Specific](#transpose_conv-constraints) |
+| UNPACK | [Generic](#generic-constraints) |
+
+## Generic Constraints
+
+This is a list of constraints that all NPU operators must satisfy in order to be scheduled on the NPU.
+
+- Input(s) and Output tensors must not be dynamic
+- Input(s) and Output tensors must have a defined shape
+- Output tensors cannot be scalar
+- Scalar Input tensors are only valid for op type: ADD, MAXIMUM, MINIMUM, MUL, SPLIT, SPLIT_V, SUB
+- Input(s) and Output tensors must not be greater than 4D
+- Tensors must be of type: {int8, int32, int16, uint8}
+- Tensors which are int32 are only valid when op type is: ADD, MUL, SUB
+- Tensor dimensions must be in the range [1, 65535]
+- Input(s), Output and Weight tensors must have quantization parameters
+- Input(s), Output and Weight tensors with quantization scales must be finite
+- Per-axis quantization is only supported for the following op types: CONV_2D, DEPTHWISE_CONV_2D, TRANSPOSE_CONV
+- The fused activation function (if present) must be one of type: LOGISTIC, RELU, RELU6, RELU_N1_TO_1, TANH
+
+## ABS Constraints
+
+This is a list of constraints that the ABS operator must satisfy in order to be scheduled on the NPU.
+
+- Batch size must be 1 for Input tensors with more than 2 dimensions
+- At least one Input's shape must match the OFM's shape
+- IFM and OFM data types must match
+
+## ADD Constraints
+
+This is a list of constraints that the ADD operator must satisfy in order to be scheduled on the NPU.
+
+- Batch size must be 1 for Input tensors with more than 2 dimensions
+- At least one Input's shape must match the OFM's shape
+- Both Input data types must match
+- For IFM that are signed, OFM must also be signed
+- For IFM that are unsigned, OFM must either be the same type or int32
+- Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
+
+## AVERAGE_POOL_2D Constraints
+
+This is a list of constraints that the AVERAGE_POOL_2D operator must satisfy in order to be scheduled on the NPU.
+
+- IFM Tensor batch size must be 1
+- Stride values for both width and height must be integer types
+- Stride values for both width and height must be in the range [1, 3]
+- IFM and OFM data types must match
+- Kernel filter values for both width and height must be integer types
+- Kernel filter values for both width and height must be in the range [1, 8]
+- VALID padding: Kernel filter height must be in the range [1, 256]
+- VALID padding: Product of kernel filter width and height must be in the range [1, 65536]
+
+## CONCATENATION Constraints
+
+This is a list of constraints that the CONCATENATION operator must satisfy in order to be scheduled on the NPU.
+
+- Axis attribute must exist
+- Axis attribute must be in the range [0, <ofm_dimensions>)
+- All Input dimensionalities must match OFM dimensionality
+- All Input dimensions must match OFM dimension in all axes except the one defined by the axis attribute
+
+## CONV_2D Constraints
+
+This is a list of constraints that the CONV_2D operator must satisfy in order to be scheduled on the NPU.
+
+- Stride values for both width and height must be integer types
+- Stride values for both width and height must be in the range [1, 3]
+- Dilation factor values for both width and height must be integer types
+- Dilation factor values for both width and height must be in the range [1, 2]
+- Dilated kernel height must be in the range [1, 64]
+- Product of dilated kernel width and height must be in the range [1, 4096]
+- Weight tensor must be 8-bit
+- Weight tensor must be constant
+- The sum of the weights cannot exceed 8323072
+- Optional Bias tensor must be of type: {int32, int64}
+- Optional Bias tensor values must fit within 40-bits
+- IFM Tensor batch size must be 1
+
+## DEPTHWISE_CONV_2D Constraints
+
+This is a list of constraints that the DEPTHWISE_CONV_2D operator must satisfy in order to be scheduled on the NPU.
+
+- Stride values for both width and height must be integer types
+- Stride values for both width and height must be in the range [1, 3]
+- Dilation factor values for both width and height must be integer types
+- Dilation factor values for both width and height must be in the range [1, 2]
+- Dilated kernel height must be in the range [1, 64]
+- Product of dilated kernel width and height must be in the range [1, 4096]
+- Weight tensor must be 8-bit
+- Weight tensor must be constant
+- The sum of the weights cannot exceed 8323072
+- Optional Bias tensor must be of type: {int32, int64}
+- Optional Bias tensor values must fit within 40-bits
+- IFM Tensor batch size must be 1
+- For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier
+
+## FULLY_CONNECTED Constraints
+
+This is a list of constraints that the FULLY_CONNECTED operator must satisfy in order to be scheduled on the NPU.
+
+- Weight tensor must be 8-bit
+- Weight tensor must be constant
+- Optional Bias tensor must be of type: {int32, int64}
+- Optional Bias tensor values must fit within 40-bits
+
+## LEAKY_RELU Constraints
+
+This is a list of constraints that the LEAKY_RELU operator must satisfy in order to be scheduled on the NPU.
+
+- Batch size must be 1 for Input tensors with more than 2 dimensions
+- At least one Input's shape must match the OFM's shape
+- IFM and OFM data types must match
+- Alpha must not be negative
+
+## MAXIMUM Constraints
+
+This is a list of constraints that the MAXIMUM operator must satisfy in order to be scheduled on the NPU.
+
+- Batch size must be 1 for Input tensors with more than 2 dimensions
+- At least one Input's shape must match the OFM's shape
+- IFM and OFM data types must match
+- Both Input quantization parameters must match OFM quantization parameters
+- Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
+
+## MAX_POOL_2D Constraints
+
+This is a list of constraints that the MAX_POOL_2D operator must satisfy in order to be scheduled on the NPU.
+
+- IFM Tensor batch size must be 1
+- Stride values for both width and height must be integer types
+- Stride values for both width and height must be in the range [1, 3]
+- IFM and OFM data types must match
+- Kernel filter values for both width and height must be integer types
+- Kernel filter height must be in the range [1, 256]
+- Product of kernel filter width and height must be in the range [1, 65536]
+
+## MINIMUM Constraints
+
+This is a list of constraints that the MINIMUM operator must satisfy in order to be scheduled on the NPU.
+
+- Batch size must be 1 for Input tensors with more than 2 dimensions
+- At least one Input's shape must match the OFM's shape
+- IFM and OFM data types must match
+- Both Input quantization parameters must match OFM quantization parameters
+- Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
+
+## MUL Constraints
+
+This is a list of constraints that the MUL operator must satisfy in order to be scheduled on the NPU.
+
+- Batch size must be 1 for Input tensors with more than 2 dimensions
+- At least one Input's shape must match the OFM's shape
+- Both Input data types must match
+- For IFM that are signed, OFM must also be signed
+- For IFM that are unsigned, OFM must either be the same type or int32
+- Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
+
+## RELU Constraints
+
+This is a list of constraints that the RELU operator must satisfy in order to be scheduled on the NPU.
+
+- The IFM quantization scale divided by the OFM quantization scale must not be infinite
+
+## RELU6 Constraints
+
+This is a list of constraints that the RELU6 operator must satisfy in order to be scheduled on the NPU.
+
+- The IFM quantization scale divided by the OFM quantization scale must not be infinite
+
+## RELU_N1_TO_1 Constraints
+
+This is a list of constraints that the RELU_N1_TO_1 operator must satisfy in order to be scheduled on the NPU.
+
+- The IFM quantization scale divided by the OFM quantization scale must not be infinite
+
+## RESIZE_BILINEAR Constraints
+
+This is a list of constraints that the RESIZE_BILINEAR operator must satisfy in order to be scheduled on the NPU.
+
+- The width and height of the IFM and OFM must match one of the following criteria:  
+        IFM W and H must both be 1  
+        IFM must match OFM  
+        OFM W and H must be 2x IFM -1, if align_corners is True  
+        OFM W and H must be 2x IFM, if align_corners is False
+
+## SOFTMAX Constraints
+
+This is a list of constraints that the SOFTMAX operator must satisfy in order to be scheduled on the NPU.
+
+- IFM and OFM shapes must match
+- IFM and OFM data types must match
+- Beta value needs to be positive
+
+## SPLIT_V Constraints
+
+This is a list of constraints that the SPLIT_V operator must satisfy in order to be scheduled on the NPU.
+
+- Only one size is allowed to be inferred
+
+## STRIDED_SLICE Constraints
+
+This is a list of constraints that the STRIDED_SLICE operator must satisfy in order to be scheduled on the NPU.
+
+- Exactly 4 Input tensors are required
+- Begin, End and Stride Input tensors must be constant
+- All Strides values must be 1
+- ellipsis_mask must be 0
+- new_axis_mask and shrink_axis_mask cannot both be set
+- Slice 'end' values must be greater than 'begin' values
+
+## SUB Constraints
+
+This is a list of constraints that the SUB operator must satisfy in order to be scheduled on the NPU.
+
+- Batch size must be 1 for Input tensors with more than 2 dimensions
+- At least one Input's shape must match the OFM's shape
+- Both Input data types must match
+- For IFM that are signed, OFM must also be signed
+- For IFM that are unsigned, OFM must either be the same type or int32
+- Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
+
+## TRANSPOSE_CONV Constraints
+
+This is a list of constraints that the TRANSPOSE_CONV operator must satisfy in order to be scheduled on the NPU.
+
+- Stride values for both width and height must be integer types
+- Stride values for both width and height must be in the range [1, 3]
+- Dilation factor values for both width and height must be integer types
+- Dilation factor values for both width and height must be in the range [1, 2]
+- Dilated kernel height must be in the range [1, 64]
+- Product of dilated kernel width and height must be in the range [1, 4096]
+- Weight tensor must be 8-bit
+- Weight tensor must be constant
+- The sum of the weights cannot exceed 8323072
+- Optional Bias tensor must be of type: {int32, int64}
+- Optional Bias tensor values must fit within 40-bits
+- IFM Tensor batch size must be 1
+- Stride values for both width and height must be 2
+- SAME padding: OFM dimensions must equal IFM dimensions multiplied by stride
+- VALID padding: OFM dimensions must equal IFM dimensions multiplied by stride,  
+                  minus difference between kernel size and stride