Optimized ml model loading using predicted usage and device state

Abstract

Loading and unloading of ML models into an ML model cache or system memory of an electronic eyewear device is managed based on which applications are active or available and predicted activities. Sensor inputs are processed to detect whether the electronic eyewear device has moved or is predicted to move and new ML models are downloaded based on updated location information or observable visual information. Sensor inputs are also processed to determine whether the electronic eyewear device has changed state or resource availability and whether the ML model cache or system memory needs to be resized to accommodate new ML models for the changed conditions. If so, stored ML models are updated to reflect the new device state by unloading an ML model, receiving a new ML model based on the changed state or resource availability and a processing priority of the new ML model, or both.