MLBEDSW-6435: Implement support for ArgMax along depth dimension

- Add support for ArgMax along depth dimension with a depth limit of 127.
- Only supports 8-bit input and 32-bit output

Signed-off-by: Rickard Bolin <rickard.bolin@arm.com>
Change-Id: I5f6f0503135bebabbb1ca637f9729587b7c60740

1 files changed, 14 insertions, 4 deletions

diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
index 860d1fe6..6f6167d2 100644
--- a/SUPPORTED_OPS.md
+++ b/SUPPORTED_OPS.md
@@ -1,7 +1,7 @@
 # Supported Ops
 
 This file was automatically generated by Vela using the `--supported-ops-report` parameter.  
-Vela version: `3.7.1.dev2+g19f8967.d20230301`
+Vela version: `3.7.1.dev8+ga182a70.d20230322`
 
 This file complies with
 [**Gitiles Markdown syntax**](https://github.com/google/gitiles/blob/master/Documentation/markdown.md)
@@ -20,6 +20,7 @@ Please check the supported operator list for your chosen runtime for further inf
 | --- | --- |
 | ABS | [Generic](#tflite-generic-constraints), [Specific](#tflite-abs-constraints) |
 | ADD | [Generic](#tflite-generic-constraints), [Specific](#tflite-add-constraints) |
+| ARG_MAX | [Generic](#tflite-generic-constraints), [Specific](#tflite-arg_max-constraints) |
 | AVERAGE_POOL_2D | [Generic](#tflite-generic-constraints), [Specific](#tflite-average_pool_2d-constraints) |
 | CONCATENATION | [Generic](#tflite-generic-constraints), [Specific](#tflite-concatenation-constraints) |
 | CONV_2D | [Generic](#tflite-generic-constraints), [Specific](#tflite-conv_2d-constraints) |
@@ -64,14 +65,14 @@ This is a list of constraints most NPU operators must satisfy in order to be sch
 - Input(s) and Output tensors must not be dynamic - [QUANTIZE]
 - Input(s) and Output tensors must have a defined shape
 - Output tensors cannot be scalar - [QUANTIZE]
-- Scalar Input tensors are only valid for op type: ADD, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, QUANTIZE, SPLIT, SPLIT_V, SUB
+- Scalar Input tensors are only valid for op type: ADD, ARG_MAX, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, QUANTIZE, SPLIT, SPLIT_V, SUB
 - Input(s) and Output tensors must not be greater than 4D
-- Input(s), Output and Weight tensors must have quantization parameters - [SHAPE]
+- Input(s), Output and Weight tensors must have quantization parameters - [ARG_MAX, SHAPE]
 - Input(s), Output and Weight tensors with quantization scales must be finite
 - Input and Output tensors must have quantization scales that fit within float32 precision
 - Constant tensors should not have NoneType-values
 - Tensors must be of type: int16, int32, int8, uint8
-- Tensors which are int32 are only valid when op type is: ADD, MUL, SHAPE, SUB
+- Tensors which are int32 are only valid when op type is: ADD, ARG_MAX, MUL, SHAPE, SUB
 - Tensor dimensions must be in the range [1, 65535]
 - Per-axis quantization is only supported for the following op types: CONV_2D, DEPTHWISE_CONV_2D, TRANSPOSE_CONV
 - IFM Tensor batch size must be 1 - [FULLY_CONNECTED, RESHAPE, SHAPE, SLICE, SOFTMAX, SPLIT, SPLIT_V, SQUEEZE, STRIDED_SLICE, UNPACK]
@@ -95,6 +96,15 @@ This is a list of constraints that the ADD operator must satisfy in order to be
 - 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
 
+### TFLite ARG_MAX Constraints
+
+This is a list of constraints that the ARG_MAX operator must satisfy in order to be scheduled on the NPU.
+
+- IFM must be int8 or uint8
+- Number of input dimensions must be 4
+- Operation must be performed along the depth axis
+- IFM depth must be no greater than 127
+
 ### TFLite 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.