SparkFun Edge Development Board – Apollo3 Blue
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SparkFun Edge Development Board – Apollo3 Blue

Part Number: SF-DEV-15170/03


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Welcome to Edge Technology! Gone are the days when data from IoT devices and application was to be processed in cloud-based platforms or data centers. With the advent of the new age edge computing, all data and applications are stored closer to the users where it is actually required. The data processing is done in microdata centers and then pushed to a cloud. This is mainly because connecting to a cloud itself requires a lot of power and connectivity that is not readily available. Sparkfuns Edge development board showcases the new edge technology that was conceived with the collaboration with Google and Ambiq. This board is perfect to get started with voice and gesture recognition. Like most advanced microcontrollers, the Apollo3 Blue has I2C/SPI masters, two UARTs, one I2C/SPI slave, a 15-channel 14-bit ADC, and a dedicated Bluetooth processor that supports BLE5. The processor ARM Cortex-M4F 48MHz (with 96MHz burst mode) processor provides the efficiency required for the microcontroller. An onboard Bluetooth antenna gives the Edge the connectivity it requires. There is also a Qwiic connector to add I2C sensors/devices, four LEDs, and four GPIO pins. The board has CR2032 coin cell holder that powers up the mode with low power consumption that can last for up to 10 days. To get started with the examples, Ambiq Micro’s Software Development Kit and Sparkfuns SDK Setup Guide is all you need.



  • 32-bit ARM Cortex-M4F processor with Direct Memory Access
  • 48MHz CPU clock, 96MHz with TurboSPOT„¢
  • Extremely low-power usage: 6uA/MHz
  • 1MB Flash
  • 384KB SRAM
  • Dedicated Bluetooth processor with BLE 5


  • ST LIS2DH12 3-axis accelerometer
  • 2x MEMS microphones with operational amplifier
  • OV7670 camera connector
  • Qwiic connector
  • 4 x GPIO connections
  • 4 x user LEDs
  • 1 x user button
  • FTDI-style serial header for programming
  • Bluetooth antenna
  • CR2032 coin cell holder for battery operation

What It Does

  • High processing to current consumption ratio enables machine learning applications on the ‘Edge’ of networks, without the need for a central computer or web connection.
  • Voice, gesture, or image recognition possible with TensorFlow Lite. (Note: Voice examples are provided. Gesture and image examples hope to be released by TensorFlow soon)


  • 1.8V – 3.6V supply voltage range
  • Small 1.6in x 1.6in x 0.35in (40.6mm x 40.6mm x 8.9mm) form factor



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