FURI | Fall 2022

Neural Networks Implementation on Embedded Devices

For this project, the goal is to understand the constraints of deploying a neural network model onto an embedded system. The idea is to train a model and deploy it to an embedded device in order to see how memory is allocated and how much information can be stored. Currently, the team has created a model based on motion and is working on deploying the model to an Arduino BLE sense board and will be working to see how the memory is used while performing inference on the model.