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author | Nikhil Raj <nikhil.raj@arm.com> | 2021-11-10 15:01:25 +0000 |
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committer | Nikhil Raj Arm <nikhil.raj@arm.com> | 2021-11-11 17:38:25 +0000 |
commit | 608c8366c0979a14607ec77d06771d6c994332b2 (patch) | |
tree | 32a492099a99c7033b92d6728f18f032028500c6 /docs/05_03_delegate.dox | |
parent | f596ad0ee83e23b3c6ad80a574be4fe7bdd4826e (diff) | |
download | armnn-608c8366c0979a14607ec77d06771d6c994332b2.tar.gz |
Remove use guide section from doxygen
* This guide has now been moved to the Quick Start section in doxygen
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I758915c43f0e9e116f7308482db34d560d7ba0d9
Diffstat (limited to 'docs/05_03_delegate.dox')
-rw-r--r-- | docs/05_03_delegate.dox | 178 |
1 files changed, 178 insertions, 0 deletions
diff --git a/docs/05_03_delegate.dox b/docs/05_03_delegate.dox new file mode 100644 index 0000000000..b3caf8cbf8 --- /dev/null +++ b/docs/05_03_delegate.dox @@ -0,0 +1,178 @@ +/// Copyright (c) 2021 ARM Limited and Contributors. All rights reserved. +/// +/// SPDX-License-Identifier: MIT +/// + +namespace armnn +{ +/** +@page delegate TfLite Delegate +@tableofcontents + + +@section delegateintro About the delegate +'armnnDelegate' is a library for accelerating certain TensorFlow Lite (TfLite) operators on Arm hardware. It can be +integrated in TfLite using its delegation mechanism. TfLite will then delegate the execution of operators supported by +Arm NN to Arm NN. + +The main difference to our @ref S6_tf_lite_parser is the amount of operators you can run with it. If none of the active +backends support an operation in your model you won't be able to execute it with our parser. In contrast to that, TfLite +only delegates operations to the armnnDelegate if it does support them and otherwise executes them itself. In other +words, every TfLite model can be executed and every operation in your model that we can accelerate will be accelerated. +That is the reason why the armnnDelegate is our recommended way to accelerate TfLite models. + +If you need help building the armnnDelegate, please take a look at our [build guide](delegate/BuildGuideNative.md). +An example how to setup TfLite to integrate the armnnDelegate can be found in this +guide: [Integrate the delegate into python](delegate/IntegrateDelegateIntoPython.md) + + +@section delegatesupport Supported Operators +This reference guide provides a list of TensorFlow Lite operators the Arm NN SDK currently supports. + +@subsection delegatefullysupported Fully supported + +The Arm NN SDK TensorFlow Lite delegate currently supports the following operators: + +- ABS + +- ADD + +- ARGMAX + +- ARGMIN + +- AVERAGE_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE + +- BATCH_TO_SPACE_ND + +- CAST + +- CONCATENATION, Supported Fused Activation: RELU , RELU6 , TANH, NONE + +- CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE + +- CONV_3D, Supported Fused Activation: RELU , RELU6 , TANH, NONE + +- DEPTH_TO_SPACE + +- DEPTHWISE_CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE + +- DEQUANTIZE + +- DIV + +- EQUAL + +- ELU + +- EXP + +- FULLY_CONNECTED, Supported Fused Activation: RELU , RELU6 , TANH, NONE + +- FLOOR + +- GATHER + +- GREATER + +- GREATER_OR_EQUAL + +- HARD_SWISH + +- LESS + +- LESS_OR_EQUAL + +- LOCAL_RESPONSE_NORMALIZATION + +- LOGICAL_AND + +- LOGICAL_NOT + +- LOGICAL_OR + +- LOGISTIC + +- LOG_SOFTMAX + +- LSTM + +- L2_NORMALIZATION + +- L2_POOL_2D + +- MAXIMUM + +- MAX_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE + +- MEAN + +- MINIMUM + +- MIRROR_PAD + +- MUL + +- NEG + +- NOT_EQUAL + +- PACK + +- PAD + +- PRELU + +- QUANTIZE + +- RANK + +- REDUCE_MAX + +- REDUCE_MIN + +- RESHAPE + +- RESIZE_BILINEAR + +- RESIZE_NEAREST_NEIGHBOR + +- RELU + +- RELU6 + +- RSQRT + +- SHAPE + +- SOFTMAX + +- SPACE_TO_BATCH_ND + +- SPACE_TO_DEPTH + +- SPLIT + +- SPLIT_V + +- SQRT + +- STRIDED_SLICE + +- SUB + +- SUM + +- TANH + +- TRANSPOSE + +- TRANSPOSE_CONV + +- UNIDIRECTIONAL_SEQUENCE_LSTM + +- UNPACK + +More machine learning operators will be supported in future releases. +**/ +}
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