EmbeddedML enables embedded systems to directly learn and recognize events through on-device neural networks. This provides embedded systems with machine learning capability operating entirely independent of other computing resources for both Neural Network training and execution.
EmbeddedML was created to be an alternative to the limited options available for Artificial Neural Networks in C. It is meant to support students design "edge" devices capabable of machine learning. The library is built to be expandable and straightforward to manipulate.
- Two examples are provided to show how a simple application could use embeddedML in a learning task.
- XOR
- XOR-AND
For those experienced with SensorTile and the STMicroelectronics Datalog project...
- Learning Orientation Based on Accelerometer Data
- Video of performance on STM's SensorTile
- Learning the XOR-AND Gate
The examples are complete EmbeddedML applications demonstrating training and testing EmbeddedML operation.
Now, for those learning about SensorTile please see the information below:
SensorTile: https://www.st.com/en/evaluation-tools/steval-stlkt01v1.html
Tutorials to get started on the SensorTile platform (along with using EmbeddedML) can be found at https://sites.google.com/view/ucla-stmicroelectronics-iot/home courtesy of Dr. Kaiser and UCLA's ECE program. These are also provided in the SensorTile Tutorials folder.