{"product_id":"sparkfun-sparkfun-edge-development-board-apollo3-blue","title":"SparkFun SparkFun Edge Development Board - Apollo3 Blue","description":"\u003cp\u003eEdge computing is here! You've probably heard of this latest entry to the long lineage of tech buzzwords like \"IoT,\" \"LoRa,\" and \"cloud\" before it, but what is �the edge� and why does it matter? The cloud is impressively powerful but all-the-time connection requires power and connectivity that may not be available. Edge computing handles discrete tasks such as determining if someone said \"yes\" and responds accordingly. The audio analysis is done at the edge rather than on the web. This dramatically reduces costs and complexity while limiting potential data privacy leaks.\u003c\/p\u003e \u003cp\u003eIn collaboration with Google and Ambiq, SparkFun's Edge Development Board is based around the newest edge technology and is perfect for getting your feet wet with voice and even gesture recognition without relying on the distant services of other companies. The truly special feature is in the utilization of Ambiq Micro's latest Apollo3 Blue microcontroller, whose ultra-efficient ARM Cortex-M4F 48MHz (with 96MHz burst mode) processor, is spec�d to run TensorFlow Lite using only 6uA\/MHz. The SparkFun Edge board currently measures ~1.6mA@3V and 48MHz and can run solely on a CR2032 coin cell battery for up to 10 days. Apollo3 Blue sports all the cutting edge features expected of modern microcontrollers including six configurable I\u003csup\u003e2\u003c\/sup\u003eC\/SPI masters, two UARTs, one I\u003csup\u003e2\u003c\/sup\u003eC\/SPI slave, a 15-channel 14-bit ADC, and a dedicated Bluetooth processor that supports BLE5. On top of all that the Apollo3 Blue has 1MB of flash and 384KB of SRAM memory - plenty for the vast majority of applications.\u003c\/p\u003e \u003cp\u003eOn the Edge you'll have built-in access to sensors, Bluetooth, I\u003csup\u003e2\u003c\/sup\u003eC expansion, and GPIO inputs\/outputs. To support edge computing cases like voice recognition the Edge board features two MEMS microphones, an ST LIS2DH12 3-axis accelerometer on its own I\u003csup\u003e2\u003c\/sup\u003eC bus, and a connector to interface to an OV7670 camera (sold separately \u0026amp; functionality coming soon). As TensorFlow updates their algorithms more and more features will open up for the SparkFun Edge. An onboard Bluetooth antenna gives the Edge out-of-the-box connectivity. Also available on the board is a Qwiic connector to add I\u003csup\u003e2\u003c\/sup\u003eC sensors\/devices, four LEDs, and four GPIO pins. To boast the low-power capabilities of the board we've outfitted it with battery operation from the CR2032 coin cell holder. Programming the board is taken care of with an external USB-serial adapter like the \u003ca href=\"https:\/\/www.sparkfun.com\/products\/15096\"\u003eSerial Basic Breakout\u003c\/a\u003e via a serial bootloader, but for more advanced users we've also made available the JTAG programming and debugger port.\u003c\/p\u003e \u003cp\u003eAs a brave explorer of this new technology, you'll have to use some advanced concepts but don't worry. Between Ambiq Micro's Software Development Kit and our \u003ca href=\"https:\/\/learn.sparkfun.com\/tutorials\/using-sparkfun-edge-board-with-ambiq-apollo3-sdk\"\u003eSDK Setup Guide\u003c\/a\u003e you'll have access to plenty of examples to begin working with your hardware.\u003c\/p\u003e \u003cp\u003eNow get out there and make something amazing with the latest machine learning technology at your very own fingertips!\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cdiv class=\"center-block text-center\"\u003e \u003ca href=\"https:\/\/learn.sparkfun.com\/tutorials\/sparkfun-edge-hookup-guide\" class=\"btn btn-default\"\u003eGet Started with the SparkFun Edge Dev Board Guide\u003c\/a\u003e \u003c\/div\u003e \u003cp\u003e\u003cstrong\u003eFeatures:\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eMicrocontroller\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003e32-bit ARM Cortex-M4F processor with Direct Memory Access\u003c\/li\u003e \u003cli\u003e48MHz CPU clock, 96MHz with TurboSPOT�\u003c\/li\u003e \u003cli\u003eExtremely low-power usage: 6uA\/MHz\u003c\/li\u003e \u003cli\u003e1MB Flash\u003c\/li\u003e \u003cli\u003e384KB SRAM\u003c\/li\u003e \u003cli\u003eDedicated Bluetooth processor with BLE 5\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eOnboard\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eST LIS2DH12 3-axis accelerometer\u003c\/li\u003e \u003cli\u003e2x MEMS microphones with operational amplifier\u003c\/li\u003e \u003cli\u003eOV7670 camera connector\u003c\/li\u003e \u003cli\u003eQwiic connector\u003c\/li\u003e \u003cli\u003e4 x GPIO connections\u003c\/li\u003e \u003cli\u003e4 x user LEDs\u003c\/li\u003e \u003cli\u003e1 x user button\u003c\/li\u003e \u003cli\u003eFTDI-style serial header for programming\u003c\/li\u003e \u003cli\u003eBluetooth antenna\u003c\/li\u003e \u003cli\u003eCR2032 coin cell holder for battery operation\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eWhat It Does\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eHigh processing to current consumption ratio enables machine learning applications on the 'Edge' of networks, without the need for a central computer or web connection.\u003c\/li\u003e \u003cli\u003eVoice, gesture, or image recognition possible with TensorFlow Lite. (Note: Voice examples are provided. Gesture and image examples hope to be released by TensorFlow soon)\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003e1.8V - 3.6V supply voltage range\u003c\/li\u003e \u003cli\u003eSmall 1.6in x 1.6in x 0.35in (40.6mm x 40.6mm x 8.9mm) form factor\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eDocuments:\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003e\u003ca href=\"https:\/\/cdn.sparkfun.com\/assets\/2\/b\/7\/4\/d\/SparkFun_Edge_Schematic.pdf\"\u003eSchematic\u003c\/a\u003e\u003c\/li\u003e \u003cli\u003e\u003ca href=\"https:\/\/cdn.sparkfun.com\/assets\/b\/1\/8\/d\/e\/SparkFun_Edge_EagleFiles.zip\"\u003eEagle Files\u003c\/a\u003e\u003c\/li\u003e \u003cli\u003e\u003ca href=\"https:\/\/learn.sparkfun.com\/tutorials\/sparkfun-edge-hookup-guide\"\u003eHookup Guide\u003c\/a\u003e\u003c\/li\u003e \u003cli\u003e\n\u003ca href=\"https:\/\/cdn.sparkfun.com\/assets\/d\/a\/7\/c\/d\/Apollo3_Blue_MCU_Data_Sheet_v0_9_1.pdf\"\u003eDatasheet\u003c\/a\u003e (Ambiq Apollo3)\u003c\/li\u003e \u003cli\u003e\u003ca href=\"https:\/\/learn.sparkfun.com\/tutorials\/using-sparkfun-edge-board-with-ambiq-apollo3-sdk\"\u003eUsing SparkFun Edge Board with Ambiq Apollo3 SDK\u003c\/a\u003e\u003c\/li\u003e \u003cli\u003e\u003ca href=\"https:\/\/codelabs.developers.google.com\/codelabs\/sparkfun-tensorflow\/#0\"\u003eMachine Learning on a Microcontroller with SparkFun Edge\u003c\/a\u003e\u003c\/li\u003e \u003cli\u003e\u003ca href=\"https:\/\/www.tensorflow.org\/lite\/guide\/microcontroller\"\u003eUsing TensorFlow on Microcontrollers\u003c\/a\u003e\u003c\/li\u003e \u003cli\u003e\u003ca href=\"https:\/\/youtu.be\/1sW4msI7ywE\"\u003eSparkFun Edge Q\u0026amp;A with Nathan Seidle \u0026amp; Pete Warden\u003c\/a\u003e\u003c\/li\u003e \u003cli\u003e\u003ca href=\"https:\/\/github.com\/sparkfun\/SparkFun_Edge\"\u003eGitHub Hardware Repo\u003c\/a\u003e\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eVideos\u003c\/strong\u003e\u003c\/p\u003e \u003cdiv class=\"flex-video-wrap clearfix\"\u003e \u003cdiv class=\"flex-video widescreen img\"\u003e \u003ciframe src=\"https:\/\/www.youtube.com\/embed\/13dmVkuk7i4\/?autohide=1\u0026amp;border=0\u0026amp;wmode=opaque\u0026amp;enablejsapi=1\" frameborder=\"0\" allowfullscreen width=\"560\" height=\"315\"\u003e\u003c\/iframe\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"flex-video-wrap clearfix\"\u003e \u003cdiv class=\"flex-video widescreen img\"\u003e \u003ciframe src=\"https:\/\/www.youtube.com\/embed\/UGspuVY62BU\/?autohide=1\u0026amp;border=0\u0026amp;wmode=opaque\u0026amp;enablejsapi=1\" frameborder=\"0\" allowfullscreen width=\"560\" height=\"315\"\u003e\u003c\/iframe\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"flex-video-wrap clearfix\"\u003e \u003cdiv class=\"flex-video widescreen img\"\u003e \u003ciframe src=\"https:\/\/www.youtube.com\/embed\/1sW4msI7ywE\/?autohide=1\u0026amp;border=0\u0026amp;wmode=opaque\u0026amp;enablejsapi=1\" frameborder=\"0\" allowfullscreen width=\"560\" height=\"315\"\u003e\u003c\/iframe\u003e \u003c\/div\u003e \u003c\/div\u003e\u003cbr\u003eAll product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.","brand":"sparkfun-10","offers":[{"title":"Default Title","offer_id":31117335461973,"sku":"15170:DEV-15170:spark","price":2940.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1034\/1611\/products\/cff71aa65fa4048edba14f3703a16724.jpg?v=1576019026","url":"https:\/\/www.tanotis.com\/products\/sparkfun-sparkfun-edge-development-board-apollo3-blue","provider":"Tanotis","version":"1.0","type":"link"}