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class='section' id='blog'><div class='widget FeaturedPost' data-version='1' id='FeaturedPost1'> </div><div class='widget Blog' data-version='1' id='Blog1'> <div class='tensorsite-container--narrow'> <div class='tensorsite-detail'> <a aria-hidden='true' class='tensorsite-detail__next-url' hidden='true' href='https://blog.tensorflow.org/2022/06/Adding-Quantization-aware-Training-and-Pruning-to-the-TensorFlow-Model-Garden.html'></a> <div aria-hidden='true' class='tensorsite-detail__current-url' hidden='true'>https://blog.tensorflow.org/2022/06/Profiling-XNNPACK-with-TFLite.html</div> <div aria-hidden='true' class='tensorsite-detail__tags' hidden='true'> <span>performance</span> <b class='label-divider-dot'>·</b> <span>profiling</span> </div> <div aria-hidden='true' class='tensorsite-detail__main-image' hidden='true'> https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGaMOZjmmcdeCHArC2JZGpTO3nsFS6Pdv4_mR7Krfqjyw5hbMWoy1TBJkDg5h9P62LPDIXoaPvj8NdwcszzXK_IhsS3Z39jx-q25Ud-Os7ShQkm2YjIhNX0Bn8R3Cfa-hcz_jZXvF_a8W9tpE2PDiX9A5d32qkAgfNpled0X_1DJuxHfoFOOtMdC4b/s1600/image6.png </div> <p aria-hidden='true' class='tensorsite-detail__description' hidden='true'> <span class='tensorsite-content__info'> June 15, 2022 — </span> <em>Posted by Alan Kelly, Software Engineer</em> We are happy to share that detailed <a href="https://www.tensorflow.org/lite/performance/measurement" target="_blank">profiling information</a> for XNNPACK is now available in TensorFlow 2.9.1 and later. <a href="https://github.com/google/XNNPACK" target="_blank">XNNPACK</a> is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms, and it is the default TensorFlow Lite CPU inference engine for floating-point models. The most common and ex… </p> <div class='tensorsite-content__subtitle'> <a href='https://blog.tensorflow.org/search?label=performance&max-results=20'> <span>performance</span> </a> <b class='label-divider-dot'>·</b> <a href='https://blog.tensorflow.org/search?label=profiling&max-results=20'> <span>profiling</span> </a> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-detail__title'> Profiling XNNPACK with TFLite </div> <div class='tensorsite-detail__contact'> <div class='tensorsite-detail__info'> <span class='tensorsite-detail__timestamp'>June 15, 2022</span> </div> <a class='icon-link' href='https://twitter.com/intent/tweet?text=%22Profiling XNNPACK with TFLite%22 from the TensorFlow Blog%0A%0Ahttps://blog.tensorflow.org/2022/06/Profiling-XNNPACK-with-TFLite.html' rel='noopener noreferrer' target='_blank' title='Share this post on Twitter'> <svg alt='Twitter Social Icon' class='twitter-icon social-icon' height='19' viewBox='0 0 23 19' width='23' xmlns='http://www.w3.org/2000/svg'> <g fill='none' fill-rule='evenodd' transform='translate(-7 -9)'> <rect height='36' width='36'></rect> <path d='M14.076,27.2827953 C22.566,27.2827953 27.21,20.2477953 27.21,14.1477953 C27.21,13.9477953 27.21,13.7487953 27.197,13.5507953 C28.1,12.8977953 28.88,12.0887953 29.5,11.1617953 C28.657,11.5347953 27.764,11.7797953 26.848,11.8877953 C27.812,11.3107953 28.533,10.4037953 28.878,9.33479527 C27.972,9.87179527 26.98,10.2507953 25.947,10.4547953 C24.198,8.59579527 21.274,8.50679527 19.415,10.2547953 C18.217,11.3817953 17.708,13.0617953 18.08,14.6647953 C14.368,14.4787953 10.91,12.7257953 8.566,9.84279527 C7.341,11.9507953 7.967,14.6497953 9.995,16.0047953 C9.261,15.9827953 8.542,15.7837953 7.9,15.4267953 L7.9,15.4847953 C7.9,17.6827953 9.449,19.5747953 11.603,20.0107953 C10.924,20.1957953 10.211,20.2227953 9.519,20.0897953 C10.124,21.9707953 11.856,23.2587953 13.832,23.2957953 C12.197,24.5797953 10.178,25.2777953 8.098,25.2747953 C7.731,25.2747953 7.364,25.2527953 7,25.2087953 C9.111,26.5627953 11.567,27.2817953 14.076,27.2787953' fill='#545454'></path> </g> </svg> </a> </div> <div class='divider divider--article-top'></div> <div class='tensorsite-detail__body'> <p><em>Posted by Alan Kelly, Software Engineer</em></p><p> </p><a name='more'></a><p></p> <p> We are happy to share that detailed <a href="https://www.tensorflow.org/lite/performance/measurement" target="_blank">profiling information</a> for XNNPACK is now available in TensorFlow 2.9.1 and later. <a href="https://github.com/google/XNNPACK" target="_blank">XNNPACK</a> is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms, and it is the default TensorFlow Lite CPU inference engine for floating-point models. </p> <p> The most common and expensive neural network operators, such as fully connected layers and convolutions, are executed by XNNPACK so that you get the best performance possible from your model. Historically the profiler would measure the runtime for the entire section of delegated graph, meaning that the runtime of all delegated operators was accumulated in one result, making it difficult to identify the individual operations that were slow. </p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGaMOZjmmcdeCHArC2JZGpTO3nsFS6Pdv4_mR7Krfqjyw5hbMWoy1TBJkDg5h9P62LPDIXoaPvj8NdwcszzXK_IhsS3Z39jx-q25Ud-Os7ShQkm2YjIhNX0Bn8R3Cfa-hcz_jZXvF_a8W9tpE2PDiX9A5d32qkAgfNpled0X_1DJuxHfoFOOtMdC4b/s1600/image6.png" style="display: block; margin-left: auto; margin-right: auto; padding: 1em 0px; text-align: center;"><img alt="" border="0" data-original-height="296" data-original-width="1999" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGaMOZjmmcdeCHArC2JZGpTO3nsFS6Pdv4_mR7Krfqjyw5hbMWoy1TBJkDg5h9P62LPDIXoaPvj8NdwcszzXK_IhsS3Z39jx-q25Ud-Os7ShQkm2YjIhNX0Bn8R3Cfa-hcz_jZXvF_a8W9tpE2PDiX9A5d32qkAgfNpled0X_1DJuxHfoFOOtMdC4b/s1600/image6.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="text-align: left;"><i>Previous TFLite profiling results when XNNPACK was used. The runtime of all delegated operators was accumulated in one row.</i></span></td></tr></tbody></table><p></p><p></p> <p> If you are using TensorFlow Lite 2.9.1 or later, it gives the per operator profile even for the section that is delegated to XNNPACK so that you no longer need to decide between fast inference and detailed performance information. The operator name, data layout (NHWC for example), datatype (FP32) and microkernel type (if applicable) are shown. </p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjC4hVKqTuz3ZI3BDW-N9XFOthfw0GAmJrCuh0QCjgXglrO69vef8Nzj8-o9NZjF5sW9_mvqg3fQa8PuQO1b14ITkAcMx2cjqfcXKAnU3CpF7L_JE7qyjt8F-SmVXS-Foug7IKB7bYpknoYu1GWhyJqey-ZLL44YtitJyBrGYbYLCLU8p1VyqKjecm_/s1563/image2.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="750" data-original-width="1563" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjC4hVKqTuz3ZI3BDW-N9XFOthfw0GAmJrCuh0QCjgXglrO69vef8Nzj8-o9NZjF5sW9_mvqg3fQa8PuQO1b14ITkAcMx2cjqfcXKAnU3CpF7L_JE7qyjt8F-SmVXS-Foug7IKB7bYpknoYu1GWhyJqey-ZLL44YtitJyBrGYbYLCLU8p1VyqKjecm_/s16000/image2.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="text-align: start;"><i>New detailed per-operator profiling information is now shown. The operator name, data layout, data type and microkernel type are visible.</i></span></td></tr></tbody></table><div style="text-align: center;"><div class="separator" style="clear: both; text-align: left;">Now, you get lots of helpful information, such as the runtime per operator and the percentage of the total runtime that it accounts for. The runtime of each node is given in the order in which they were executed. The most expensive operators are also listed.</div></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjszotPDs2GEy4drnKKcO1gPTCmmEYpf60Yk3iTA2MCogHpXF-pBWUYsH__DIoFkWhJisBBTP6uebX3MAyC0XFthmV5vcGFBndJF0L1EodeESG4tMJ9uY9z0IjotVNySAjcghi40WGRLZOFyneNB2J96pXlMEMXijMxRikoT68yzL1j1jgBMygupjWV/s1600/image3.png" style="display: block; padding: 1em 0px; text-align: center;"><img alt="" border="0" data-original-height="220" data-original-width="1475" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjszotPDs2GEy4drnKKcO1gPTCmmEYpf60Yk3iTA2MCogHpXF-pBWUYsH__DIoFkWhJisBBTP6uebX3MAyC0XFthmV5vcGFBndJF0L1EodeESG4tMJ9uY9z0IjotVNySAjcghi40WGRLZOFyneNB2J96pXlMEMXijMxRikoT68yzL1j1jgBMygupjWV/s1600/image3.png" /></a></div></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="text-align: left;"><i>The most expensive operators are listed. In this example, you can see that a deconvolution accounted for 33.91% of the total runtime.</i></span></td></tr></tbody></table><p></p><p></p> <p> XNNPACK can also perform inference in half-precision (16 bit) floating point format if the hardware supports these operations natively, and IEEE16 inference is supported for every floating-point operator in the model, and the model’s `reduced_precision_support` metadata indicates that it is compatible with FP16 inference. FP16 inference can also be forced. More information is available <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/xnnpack/README.md#floating-point-ieee-fp16-operators-experimental" target="_blank">here</a>. If half precision has been used, then F16 will be present in the Name column:</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxQNnx-7gGV730igjxQLFBYhj8EH9B7FXaiRmJ61E2bD5lDfpwJ_6UFC0ViXc_EdjX4bpuxJkSDfhrRuHvu9UB0-GRYsyF9co3aqIpYBDyh2wQVq0_7yKDaFGwSN2om7c18piUo_6SYD5uU6N4J1yzzjiBbeM4u1krWhzTOTkiuHlNCi1NUUSxp3a6/s1600/image5.png" style="display: block; padding: 1em 0px; text-align: center;"><img alt="" border="0" data-original-height="220" data-original-width="1504" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxQNnx-7gGV730igjxQLFBYhj8EH9B7FXaiRmJ61E2bD5lDfpwJ_6UFC0ViXc_EdjX4bpuxJkSDfhrRuHvu9UB0-GRYsyF9co3aqIpYBDyh2wQVq0_7yKDaFGwSN2om7c18piUo_6SYD5uU6N4J1yzzjiBbeM4u1krWhzTOTkiuHlNCi1NUUSxp3a6/s1600/image5.png" /></a></div></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="text-align: left;"><i>FP16 inference has been used.</i></span></td></tr></tbody></table><p></p><p></p> <p> Here, unsigned quantized inference has been used (QU8).</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5mSDtQ3FzM_t0tuazFhgIEOdjRhbZG_ReGkegnKKmqfIeEXAzVwS3Xcl-SPFz6EoBFsgx2SlZWWOJ5wMOg-Jra8_hvZjAE_9eAB-3XVgDcv2qOHYDPqMzk7XIynvAA2qMWAEwPWgjwh5uaWpVePcHG4UyxXU7kjPI7VqaLBf0zh5WOlugtT7boDdN/s1600/TF%20Blog.png" style="display: block; padding: 1em 0px; text-align: center;"><img alt="" border="0" data-original-height="220" data-original-width="1475" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5mSDtQ3FzM_t0tuazFhgIEOdjRhbZG_ReGkegnKKmqfIeEXAzVwS3Xcl-SPFz6EoBFsgx2SlZWWOJ5wMOg-Jra8_hvZjAE_9eAB-3XVgDcv2qOHYDPqMzk7XIynvAA2qMWAEwPWgjwh5uaWpVePcHG4UyxXU7kjPI7VqaLBf0zh5WOlugtT7boDdN/s1600/TF%20Blog.png" /></a></div></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="text-align: left;"><i>QU8 indicates that unsigned quantized inference has been used</i></span></td></tr></tbody></table><p></p><p></p> <p> And finally, sparse inference has been used. Sparse operators require that the data layout change from NHWC to NCHW as this is more efficient. This can be seen in the operator name.</p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJW-1poK1ASuATN0IjayI53Hj6OYeyWvsuQdPOitXVjNbtdJiMpOK6jQVAK5QBTyeoCvsiZqMG93SrA9NZNyk2hVHEWWgF_7PILtAIdgBIAni-UWivl07GhDxYqYzMt8hDsO1PbjVMsaBgGIet5oTcbe0kijN9t-QIqtdIYVStmIExc_RaSZoOCng1/s1600/image1.png" style="display: block; padding: 1em 0px; text-align: center;"><img alt="" border="0" data-original-height="228" data-original-width="1507" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJW-1poK1ASuATN0IjayI53Hj6OYeyWvsuQdPOitXVjNbtdJiMpOK6jQVAK5QBTyeoCvsiZqMG93SrA9NZNyk2hVHEWWgF_7PILtAIdgBIAni-UWivl07GhDxYqYzMt8hDsO1PbjVMsaBgGIet5oTcbe0kijN9t-QIqtdIYVStmIExc_RaSZoOCng1/s1600/image1.png" /></a></div></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="text-align: left;"><i>SPMM microkernel indicates that the operator is evaluated via SParse matrix-dense Matrix Multiplication. Note that sparse inference use NCHW layout (vs the typical NHWC) for the operators.</i></span></td></tr></tbody></table><p></p><p></p> <p>Note that when some operators are delegated to XNNPACK, and others aren’t, two sets of profile information are shown. This happens when not all operators in the model are supported by XNNPACK. The next step in this project is to merge profile information from XNNPACK operators and TensorFlow Lite into one profile.</p> <h2><strong>Next Steps</strong></h2> <p>You can learn more about performance measurement and profiling in TensorFlow Lite by visiting this <a href="https://www.tensorflow.org/lite/performance/measurement" target="_blank">guide</a>. Thanks for reading!</p><br /> </div> </div> <div class='tensorsite-detail-footer'> <div class='article-divider'> <img alt='Diamond Article Divider' src='https://www.gstatic.com/tf_blog/images/ic_article_end.svg'/> </div> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=performance&max-results=20'> performance </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=profiling&max-results=20'> profiling </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=TensorFlow+Lite&max-results=20'> TensorFlow Lite </a> </div> </div> <div class='tensorsite-next'> <div class='tensorsite-container--large'> <div class='tensorsite-next__title'>Next post</div> <div class='tensorsite-card'> <a aria-label='Next Card' class='tensorsite-card__href next' href='https://blog.tensorflow.org/2022/06/Profiling-XNNPACK-with-TFLite.html'></a> <div class='tensorsite-content__image-wrapper'> <img alt='Profiling XNNPACK with TFLite' class='tensorsite-content__image' src='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGaMOZjmmcdeCHArC2JZGpTO3nsFS6Pdv4_mR7Krfqjyw5hbMWoy1TBJkDg5h9P62LPDIXoaPvj8NdwcszzXK_IhsS3Z39jx-q25Ud-Os7ShQkm2YjIhNX0Bn8R3Cfa-hcz_jZXvF_a8W9tpE2PDiX9A5d32qkAgfNpled0X_1DJuxHfoFOOtMdC4b/s1600/image6.png'/> </div> <div class='tensorsite-content'> <div class='tensorsite-content__subtitle next'> <span>performance</span> <b class='label-divider-dot'>·</b> <span>profiling</span> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-content__title next'> Profiling XNNPACK with TFLite </div> <p class='tensorsite-content__description next'> <span class='tensorsite-content__info'> June 15, 2022 </span> — <span> <em>Posted by Alan Kelly, Software Engineer</em> We are happy to share that detailed <a href="https://www.tensorflow.org/lite/performance/measurement" target="_blank">profiling information</a> for XNNPACK is now available in TensorFlow 2.9.1 and later. <a href="https://github.com/google/XNNPACK" target="_blank">XNNPACK</a> is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms, and it is the default TensorFlow Lite CPU inference engine for floating-point models. 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