SparkFun MicroMod Machine Learning Carrier Board
The MicroMod Machine Learning Carrier Board combines some of the features of our SparkFun Edge Board and SparkFun Artemis boards, but allows you the freedom to explore with any processor in the MicroMod lineup without the need for a central computer or web connection.
Voice recognition, always-on voice commands, gesture, or image recognition are possible with TensorFlow applications. 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 on the MicroMod combination rather than on the web. This dramatically reduces costs and complexity while limiting potential data privacy leaks.
This board features two MEMS microphones (one with a PDM interface, one with an I2S interface), an ST LIS2DH12 3-axis accelerometer, a connector to interface to a camera (sold separately), and a Qwiic connector. A modern USB-C connector makes programming easy and we've exposed the JTAG connector for more advanced users who prefer to use the power and speed of professional tools. We've even added a convenient jumper to measure current consumption for low power testing.
Get Started with the Micromod Machine Learning Carrier Board Guide
Features
- M.2 MicroMod Keyed-E H4.2mm 65 pin SMD Connector 0.5mm
- Digital I2C MEMS Microphone PDM Invensense ICS-43434 (COMP)
- Digital PDM MEMS Microphone PDM Knowles SPH0641LM4H-1 (IC)
- ML414H-IV01E Lithium Battery for RTC
- ST LIS2DH12TR Accelerometer (3-axis, ultra-low-power)
- 24 Pin 0.5mm FPC Connector (Himax camera connector)
- USB - C
- Qwiic connector
- MicroSD socket
- Phillips #0 M2.5x3mm screw included
Resources
MicroMod Machine Learning Carrier Documentation:
MicroMod Documentation:
Original: $17.70
-65%$17.70
$6.19
Description
The MicroMod Machine Learning Carrier Board combines some of the features of our SparkFun Edge Board and SparkFun Artemis boards, but allows you the freedom to explore with any processor in the MicroMod lineup without the need for a central computer or web connection.
Voice recognition, always-on voice commands, gesture, or image recognition are possible with TensorFlow applications. 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 on the MicroMod combination rather than on the web. This dramatically reduces costs and complexity while limiting potential data privacy leaks.
This board features two MEMS microphones (one with a PDM interface, one with an I2S interface), an ST LIS2DH12 3-axis accelerometer, a connector to interface to a camera (sold separately), and a Qwiic connector. A modern USB-C connector makes programming easy and we've exposed the JTAG connector for more advanced users who prefer to use the power and speed of professional tools. We've even added a convenient jumper to measure current consumption for low power testing.
Get Started with the Micromod Machine Learning Carrier Board Guide
Features
- M.2 MicroMod Keyed-E H4.2mm 65 pin SMD Connector 0.5mm
- Digital I2C MEMS Microphone PDM Invensense ICS-43434 (COMP)
- Digital PDM MEMS Microphone PDM Knowles SPH0641LM4H-1 (IC)
- ML414H-IV01E Lithium Battery for RTC
- ST LIS2DH12TR Accelerometer (3-axis, ultra-low-power)
- 24 Pin 0.5mm FPC Connector (Himax camera connector)
- USB - C
- Qwiic connector
- MicroSD socket
- Phillips #0 M2.5x3mm screw included
Resources
MicroMod Machine Learning Carrier Documentation:
MicroMod Documentation:
