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<div class="title">TfLite Delegate </div> </div>
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<div class="toc"><h3>Table of Contents</h3>
<ul><li class="level1"><a href="#delegateintro">About the delegate</a></li>
<li class="level1"><a href="#delegatesupport">Supported Operators</a><ul><li class="level2"><a href="#delegatefullysupported">Fully supported</a></li>
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<div class="textblock"><h1><a class="anchor" id="delegateintro"></a>
About the delegate</h1>
<p>'<a class="el" href="namespacearmnn_delegate.xhtml">armnnDelegate</a>' 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.</p>
<p>The main difference to our <a class="el" href="parsers.xhtml#S6_tf_lite_parser">ArmNN Tf Lite Parser</a> 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 <a class="el" href="namespacearmnn_delegate.xhtml">armnnDelegate</a> 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 <a class="el" href="namespacearmnn_delegate.xhtml">armnnDelegate</a> is our recommended way to accelerate TfLite models.</p>
<p>If you need help building the <a class="el" href="namespacearmnn_delegate.xhtml">armnnDelegate</a>, please take a look at our <a class="el" href="md_delegate__build_guide_native.xhtml">build guide</a>. An example how to setup TfLite to integrate the <a class="el" href="namespacearmnn_delegate.xhtml">armnnDelegate</a> can be found in this guide: <a class="el" href="md_delegate__integrate_delegate_into_python.xhtml">Integrate the delegate into python</a></p>
<h1><a class="anchor" id="delegatesupport"></a>
Supported Operators</h1>
<p>This reference guide provides a list of TensorFlow Lite operators the Arm NN SDK currently supports.</p>
<h2><a class="anchor" id="delegatefullysupported"></a>
Fully supported</h2>
<p>The Arm NN SDK TensorFlow Lite delegate currently supports the following operators:</p>
<ul>
<li>ABS</li>
<li>ADD</li>
<li>ARGMAX</li>
<li>ARGMIN</li>
<li>AVERAGE_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li>
<li>BATCH_TO_SPACE_ND</li>
<li>CONCATENATION, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li>
<li>CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li>
<li>DEPTH_TO_SPACE</li>
<li>DEPTHWISE_CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li>
<li>DEQUANTIZE</li>
<li>DIV</li>
<li>EQUAL</li>
<li>ELU</li>
<li>EXP</li>
<li>FULLY_CONNECTED, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li>
<li>FLOOR</li>
<li>GATHER</li>
<li>GREATER</li>
<li>GREATER_OR_EQUAL</li>
<li>HARD_SWISH</li>
<li>LESS</li>
<li>LESS_OR_EQUAL</li>
<li>LOCAL_RESPONSE_NORMALIZATION</li>
<li>LOGICAL_AND</li>
<li>LOGICAL_NOT</li>
<li>LOGICAL_OR</li>
<li>LOGISTIC</li>
<li>LOG_SOFTMAX</li>
<li>LSTM</li>
<li>L2_NORMALIZATION</li>
<li>L2_POOL_2D</li>
<li>MAXIMUM</li>
<li>MAX_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE</li>
<li>MEAN</li>
<li>MINIMUM</li>
<li>MUL</li>
<li>NEG</li>
<li>NOT_EQUAL</li>
<li>PAD</li>
<li>PRELU</li>
<li>QUANTIZE</li>
<li>RANK</li>
<li>REDUCE_MAX</li>
<li>REDUCE_MIN</li>
<li>RESHAPE</li>
<li>RESIZE_BILINEAR</li>
<li>RESIZE_NEAREST_NEIGHBOR</li>
<li>RELU</li>
<li>RELU6</li>
<li>RSQRT</li>
<li>SOFTMAX</li>
<li>SPACE_TO_BATCH_ND</li>
<li>SPACE_TO_DEPTH</li>
<li>SPLIT</li>
<li>SPLIT_V</li>
<li>SQRT</li>
<li>STRIDED_SLICE</li>
<li>SUB</li>
<li>SUM</li>
<li>TANH</li>
<li>TRANSPOSE</li>
<li>TRANSPOSE_CONV</li>
</ul>
<p>More machine learning operators will be supported in future releases. </p>
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