Online On-Device MCU Transfer Learning

This project develops a method to improve the performance and flexibility of microcontroller (MCU) on-device binary image classification (MobileNetV1) by implementing online on-device transfer learning. Given the necessary energy, memory and compute efficiency, training deep networks on MCUs seems far-fetched and near impossible. Instead, online transfer learning is an implementable edge learning mechanism that can make use of the feature detection capabilities of a well-trained frozen base model without having to gather/store/access large datasets and parameter values on device.