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https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi56Z-LGz6atWKJyZYrOliUcQ9ZmqDnpWRCGr7VfmfWkFhT7ZANiWRymE3P110_t25ClB5hgO1Fw00dZwe0q4Bzzk7yci4J1RHvZ2C35U87SYvYKbT5tKkbdIoDQGLU6JbD8JXDbrdTYEv3jJkmKofxSU25-khUhrL5WfwqoaFLummdh7ab3k0gZgih/s1600/micro-inhand_fafafa.jpg </div> <p aria-hidden='true' class='tensorsite-detail__description' hidden='true'> <span class='tensorsite-content__info'> February 02, 2023 — </span> <em>Posted by Scott Main, Technical Writer, and the Coral team</em> In just a few years, ML models for mobile and embedded systems have come a very long way. With TensorFlow Lite (TFLite), you can now run sophisticated models that perform pose estimation and object segmentation, but these models still require a relatively powerful processor and a high-level OS in a mobile device or small computer like a R… </p> <div class='tensorsite-content__subtitle'> <a href='https://blog.tensorflow.org/search?label=Coral&max-results=20'> <span>Coral</span> </a> <b class='label-divider-dot'>·</b> <a href='https://blog.tensorflow.org/search?label=TensorFlow+Lite&max-results=20'> <span>TensorFlow Lite</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'> TensorFlow Lite Micro with ML acceleration </div> <div class='tensorsite-detail__contact'> <div class='tensorsite-detail__info'> <span class='tensorsite-detail__timestamp'>February 02, 2023</span> </div> <a class='icon-link' href='https://twitter.com/intent/tweet?text=%22TensorFlow Lite Micro with ML acceleration%22 from the TensorFlow Blog%0A%0Ahttps://blog.tensorflow.org/2023/02/tensorflow-lite-micro-with-ml-acceleration.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'> <meta content="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi56Z-LGz6atWKJyZYrOliUcQ9ZmqDnpWRCGr7VfmfWkFhT7ZANiWRymE3P110_t25ClB5hgO1Fw00dZwe0q4Bzzk7yci4J1RHvZ2C35U87SYvYKbT5tKkbdIoDQGLU6JbD8JXDbrdTYEv3jJkmKofxSU25-khUhrL5WfwqoaFLummdh7ab3k0gZgih/s1600/micro-inhand_fafafa.jpg" name="twitter:image"></meta> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi56Z-LGz6atWKJyZYrOliUcQ9ZmqDnpWRCGr7VfmfWkFhT7ZANiWRymE3P110_t25ClB5hgO1Fw00dZwe0q4Bzzk7yci4J1RHvZ2C35U87SYvYKbT5tKkbdIoDQGLU6JbD8JXDbrdTYEv3jJkmKofxSU25-khUhrL5WfwqoaFLummdh7ab3k0gZgih/s1600/micro-inhand_fafafa.jpg" style="display: none;" /> <p><em>Posted by Scott Main, Technical Writer, and the Coral team</em></p><p> </p><a name='more'></a><p></p> <p> In just a few years, ML models for mobile and embedded systems have come a very long way. With TensorFlow Lite (TFLite), you can now run sophisticated models that perform pose estimation and object segmentation, but these models still require a relatively powerful processor and a high-level OS in a mobile device or small computer like a Raspberry Pi. Alternatively, you can use <a href="https://www.tensorflow.org/lite/microcontrollers" target="_blank">TensorFlow Lite Micro</a> (TFLM) on low-power microcontrollers (MCUs) to run simple models such as image and audio classification. However, the models for MCUs are much smaller, so they have limited capabilities and accuracy. </p> <p> So there's an opportunity cost when you must select between TFLM (low power but limited model performance) and regular TFLite (great model performance but higher power cost). Wouldn't it be nice if you could get both on one board? Well, we're happy to announce that the <a href="https://coral.ai/products/dev-board-micro" target="_blank">Coral Dev Board Micro</a> is now available to provide exactly that. </p> <h3 style="text-align: left;">A tiny board with big muscle</h3> <p> The <a href="https://coral.ai/products/dev-board-micro" target="_blank">Dev Board Micro</a> is a microcontroller board (with a dual-core Cortex-M7 and Cortex-M4), so it's small and power efficient, but it also includes the Coral Edge TPU™ on board, so it offers outstanding inferencing speeds for larger TFLite models. Plus, it has an on-board camera (324x324) and microphone. Naturally, there are plenty of GPIO pins and high-density connectors for add-on boards (such as our own <a href="https://coral.ai/products/wireless-add-on" target="_blank">Wireless Add-on</a> and <a href="https://coral.ai/products/poe-add-on/" target="_blank">PoE Add-on</a>). </p> <div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><center><img alt="against a nebulous bright white background, a hand holding up a chip board with the words 'Dev Board Micro' and the Coral Logo on it between the thumb and index finger" border="0" data-original-height="1504" data-original-width="720" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi56Z-LGz6atWKJyZYrOliUcQ9ZmqDnpWRCGr7VfmfWkFhT7ZANiWRymE3P110_t25ClB5hgO1Fw00dZwe0q4Bzzk7yci4J1RHvZ2C35U87SYvYKbT5tKkbdIoDQGLU6JbD8JXDbrdTYEv3jJkmKofxSU25-khUhrL5WfwqoaFLummdh7ab3k0gZgih/s1600/micro-inhand_fafafa.jpg" style="width: 100%;" td="" /></center></td></tr><tr><td class="tr-caption" style="text-align: center;"><i></i></td></tr></tbody></table></div> <p> The Dev Board Micro executes your models using TFLM, which supports only a subset of operations in TFLite. Even if TFLM did support all the same ops, the MCU would still be much too slow for practical applications that use complex models such as for object detection and pose estimation. However, when you compile a TFLite model for the Edge TPU, all the MCU needs to do is set the model's input, delegate the model ops to the Edge TPU, and then read the output. </p> <div style="text-align: left;"> As such, even though you're still using the smaller TFLM interpreter, you can run sophisticated TFLite models that otherwise are not compatible with the TFLM interpreter, because they actually execute on the Edge TPU. For example, with the Dev Board Micro, you can run PoseNet for pose estimation, BodyPix for body segmentation, SSD MobileNet for object detection, and <a href="https://coral.ai/models/" target="_blank">much more</a>, at realtime speeds. For example: </div> <div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><center><img alt="Table showing the different models with corresponding inference time on Dev Board Micro with Edge TPU" border="0" data-original-height="1504" data-original-width="720" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiGWHiSXXNHpqOdFWwZuNq-Z6xtiFmRPoGhPHzgXrkSdU293lvclIa_kpGhn-pxfy7bAfyF0AMRIDzVtLuq4tQSBZDZiE0DKgeJtGILDfwsbdDbXGWoG1jGY6tBtIG5UM1S_xvln447MttV7uMULGmkTRuUbzzGZImj3-2-3BwxHPNi53FPoYj91rwP/s1600/Screen%20Shot%202022-12-14%20at%205.31.38%20PM.png" style="width: 100%;" td="" /></center></td></tr><tr><td class="tr-caption" style="text-align: center;"><i></i></td></tr></tbody></table></div> <div style="text-align: left;"> Of course, running the Edge TPU demands more power, but the beauty of this board's dual-core MCU is that you can run low-power apps on the M4 (which supports tiny TFLM models) and then activate the M7 and Edge TPU only as needed to run more sophisticated TFLite models. </div><div style="text-align: left;"><br /></div> <div style="text-align: left;"> To better understand how this board compares to our other Coral board, here's a brief comparison of our different developer boards: </div> <div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><center><img alt="Table comparing the price (USD), size, processor,RAM, camera, microphone, wi-fi/bluetooth, ethernet, and operating system capabilities across Dev Board Micro, Dev Board Mini and Dev Board" border="0" data-original-height="1504" data-original-width="720" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh96QZGP9LiFT5xlsXgqIuqudpXy_0SBX01TtdeW-D8j9Zac8N5TCoC5PQIppOPcnVhRhx5VYxEgziDFvXFsPodXA47DjPbak6I__3hzcTljSREYPDH48h3MiT3sPaF5X-RULbRRzr2JCSGKKYnjnwcRLUuL8mUSjsN3tv3YkSNKRCUFhzaGGqCCvTY/s2052/Screen%20Shot%202022-12-14%20at%205.34.12%20PM.png" style="width: 100%;" td="" /></center></td></tr><tr><td class="tr-caption" style="text-align: center;"><i></i></td></tr></tbody></table></div> <h3 style="text-align: left;">Get started</h3> <p> We built a new platform for the Dev Board Micro based on FreeRTOS and included compatibility with the Arduino programming language. So you can build a C++ app with CMake and flash it to the board with our command line tools, or you can write and upload an Arduino sketch with the Arduino IDE. We call this new platform coralmicro and it's fully <a href="https://github.com/google-coral/coralmicro" target="_blank">open sourced on GitHub</a>. </p> <p> If you choose to code with FreeRTOS, coralmicro includes all the core FreeRTOS APIs you need to build multi-tasking apps on the MCU, plus custom <a href="https://coral.ai/docs/reference/micro/" target="_blank">coralmicro APIs</a> for interacting with GPIOs, capturing photos, listening to audio, performing multi-core processing, and much more. </p> <p> Because coralmicro uses <a href="https://www.tensorflow.org/lite/microcontrollers" target="_blank">TensorFlow Lite for Microcontrollers</a> for inferencing, running a TensorFlow Lite model on the Dev Board Micro works almost exactly the way you expect, if you've used TensorFlow Lite on other platforms. One difference with TFLM, compared to TFLite, is that you need to specify the ops used by your model by adding them to the <span style="background-color: #eeeeee; font-family: courier;">MicroMutableOpResolver</span>. For example, if your model uses 2D convolution, then you need to call <a href="https://github.com/tensorflow/tflite-micro/blob/24c08505dfd2a97b343220bd1c4006f881061ea6/tensorflow/lite/micro/micro_mutable_op_resolver.h#L192" target="_blank"><span style="background-color: #eeeeee; font-family: courier;">AddConv2D()</span></a>. This way, you conserve memory by compiling only the op kernels you actually need to run your model on the MCU. However, if your model is compiled to run on the Edge TPU, then you also need to add the Edge TPU custom op, which accounts for all the ops that run on the Edge TPU. For example, when using SSD MobileNet for object detection on the Edge TPU, only the dequantize and post-processing ops run on the MCU, and the rest are delegated to the Edge TPU custom op, so the code to set up the <span style="background-color: #eeeeee; font-family: courier;">MicroInterpreter</span> looks like this: </p><pre class="auto hljs mipsasm" id="preview" style="background: rgb(240, 240, 240); height: 16pc; overflow: auto scroll; padding: 0.5em;"><span style="font-family: courier;"><span style="color: #444444;">auto tpu_context = coralmicro::EdgeTpuManager::GetSingleton()->OpenDevice()<span class="hljs-comment" style="color: #888888;">;</span> if (!tpu_context) { printf(<span class="hljs-string" style="color: #880000;">"ERROR: Failed to get EdgeTpu context\r\n"</span>)<span class="hljs-comment" style="color: #888888;">;</span> vTaskSuspend(nullptr)<span class="hljs-comment" style="color: #888888;">;</span> } <span class="hljs-symbol" style="color: #bc6060;"> tflite:</span>:MicroErrorReporter error_reporter<span class="hljs-comment" style="color: #888888;">;</span> <span class="hljs-symbol" style="color: #bc6060;">tflite:</span>:MicroMutableOpResolver<<span class="hljs-number" style="color: #880000;">3</span>> resolver<span class="hljs-comment" style="color: #888888;">;</span> resolver.<span class="hljs-keyword" style="font-weight: 700;">AddDequantize(); </span>resolver.<span class="hljs-keyword" style="font-weight: 700;">AddDetectionPostprocess(); </span>resolver.<span class="hljs-keyword" style="font-weight: 700;">AddCustom(coralmicro::kCustomOp, </span>coralmicro::RegisterCustomOp())<span class="hljs-comment" style="color: #888888;">;</span> <span class="hljs-symbol" style="color: #bc6060;"> tflite:</span>:MicroInterpreter interpreter(tflite::GetModel(model<span class="hljs-meta" style="color: #1f7199;">.data</span>()), resolver, tensor_arena, kTensorArenaSize, &error_reporter)</span><span style="color: #888888;">;</span></span></pre><p> Notice that you also need to turn on the Edge TPU with <a href="https://coral.ai/docs/reference/micro/tensorflow/#_CPPv4N10coralmicro14EdgeTpuManager10OpenDeviceE15PerformanceMode" target="_blank"><span style="background-color: #eeeeee; font-family: courier;">OpenDevice()</span></a>. Other than that and <code><a href="https://coral.ai/docs/reference/micro/tensorflow/#_CPPv4N6tflite22MicroMutableOpResolver9AddCustomEPKcP18TfLiteRegistration" target="_blank"><span style="background-color: #eeeeee; font-family: courier;">AddCustom()</span></a></code>, the code to run an inference on the Dev Board Micro is pretty standard TensorFlow code. For more details, see our <a href="https://coral.ai/docs/reference/micro/tensorflow/" target="_blank">API reference for TFLM</a>, and check out our <a href="https://github.com/google-coral/coralmicro/tree/main/examples" target="_blank">code examples</a> for FreeRTOS. </p><p> If you prefer to code with the Arduino IDE, we offer <a href="https://coral.ai/docs/reference/micro/arduino/" target="_blank">Arduino-style APIs</a> for most of the same features available in FreeRTOS (multi-core processing is not available in Arduino). All you need to do is install the "Coral" boards package in the Arduino IDE's Board Manager, select the Dev Board Micro board, and then you can browse all our examples for the Dev Board Micro in <strong>File > Examples</strong>. </p> <div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><center><img alt="Table comparing the price (USD), size, processor,RAM, camera, microphone, wi-fi/bluetooth, ethernet, and operating system capabilities across Dev Board Micro, Dev Board Mini and Dev Board" border="0" data-original-height="1504" data-original-width="720" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh96QZGP9LiFT5xlsXgqIuqudpXy_0SBX01TtdeW-D8j9Zac8N5TCoC5PQIppOPcnVhRhx5VYxEgziDFvXFsPodXA47DjPbak6I__3hzcTljSREYPDH48h3MiT3sPaF5X-RULbRRzr2JCSGKKYnjnwcRLUuL8mUSjsN3tv3YkSNKRCUFhzaGGqCCvTY/s2052/Screen%20Shot%202022-12-14%20at%205.34.12%20PM.png" style="width: 100%;" td="" /></center></td></tr><tr><td class="tr-caption" style="text-align: center;"><i></i></td></tr></tbody></table></div> <p> You can learn more about the board and <a href="https://coral.ai/products/dev-board-micro" target="_blank">find a seller here</a>, and start running the code examples by following our <a href="https://coral.ai/docs/dev-board-micro/get-started/" target="_blank">get started guide</a>. </p> <p></p><p></p><p></p><p></p> </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=Coral&max-results=20'> Coral </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=TensorFlow+Lite&max-results=20'> TensorFlow Lite </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=tinyML&max-results=20'> tinyML </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/2023/02/tensorflow-lite-micro-with-ml-acceleration.html'></a> <div class='tensorsite-content__image-wrapper'> <img alt='TensorFlow Lite Micro with ML acceleration' class='tensorsite-content__image' src='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi56Z-LGz6atWKJyZYrOliUcQ9ZmqDnpWRCGr7VfmfWkFhT7ZANiWRymE3P110_t25ClB5hgO1Fw00dZwe0q4Bzzk7yci4J1RHvZ2C35U87SYvYKbT5tKkbdIoDQGLU6JbD8JXDbrdTYEv3jJkmKofxSU25-khUhrL5WfwqoaFLummdh7ab3k0gZgih/s1600/micro-inhand_fafafa.jpg'/> </div> <div class='tensorsite-content'> <div class='tensorsite-content__subtitle next'> <span>Coral</span> <b class='label-divider-dot'>·</b> <span>TensorFlow Lite</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'> TensorFlow Lite Micro with ML acceleration </div> <p class='tensorsite-content__description next'> <span class='tensorsite-content__info'> February 02, 2023 </span> — <span> <em>Posted by Scott Main, Technical Writer, and the Coral team</em> In just a few years, ML models for mobile and embedded systems have come a very long way. With TensorFlow Lite (TFLite), you can now run sophisticated models that perform pose estimation and object segmentation, but these models still require a relatively powerful processor and a high-level OS in a mobile device or small computer like a R… </span> </p> </div> </div> </div> </div>  </div></div> </div>  <div class='tensorsite-full-footer'> <div class='section' id='footer'><div class='widget HTML' data-version='1' id='HTML2'> <section class='tensorsite-footer'> <div class='tensorsite-container tensorsite-footer__container'> <div class='tensorsite-footer__side tensorsite-footer__side--left'></div> <div class='tensorsite-footer__side tensorsite-footer__side--right'></div> <div class='tensorsite-footer__content'> <div class='tensorsite-content'> <div class='tensorsite-content__title tensorsite-content__title--grow'> Build, deploy, and experiment easily with TensorFlow </div> <div class='tensorsite-content__cta-wrapper'> <a class='tensorsite-content__button tensorsite-button 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