Predictive ML Model Caching for Electronic Eyewear Latency
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Solution Overview
Problem
Wearable electronic devices like electronic eyewear are resource-constrained and latency-sensitive, requiring efficient management of machine learning models to optimize performance across varying environments and connectivity conditions.
Innovation Solution
A system that manages the loading and unloading of ML models based on device state, network availability, and geolocation, using sensor inputs to predict and adapt ML model caching to ensure optimal performance and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple ML models are pre-loaded into wearable device memory, then model availability and service responsiveness are improved, but device memory consumption and power usage increase
Solution Approach 1:
The system performs preliminary actions by pre-loading ML models into the wearable device's memory before they are actually needed. The model management system predicts which models will be required based on device state, network conditions, and usage patterns, and proactively loads them into memory in advance. This ensures models are immediately available when needed while allowing the system to optimize which models to load based on predicted requirements rather than loading all possible models.
2Speed
If ML models are loaded into wearable device memory, then processing speed and latency are improved, but device power consumption increases
Solution Approach 1:
The system dynamically adjusts the loading and unloading of ML models based on real-time device conditions. The model management system continuously monitors device state, network availability, and power levels, and adapts the model caching strategy accordingly. Models are loaded into memory when power is abundant and processing speed is prioritized, and unloaded when power conservation is needed, creating a dynamic balance between performance and energy consumption.
3Loss of time
If ML models are cached locally in wearable devices, then service responsiveness and user experience are improved, but device storage capacity is consumed
Solution Approach 1:
The system changes the parameter of model storage location dynamically. Instead of permanently storing models locally, the model management system transitions models between different storage states: frequently used models are cached in local memory for fast access, while less frequently used models are stored remotely in the cloud. The system adjusts the caching duration and size based on model usage patterns, device storage availability, and network conditions, optimizing the balance between local caching benefits and storage constraints.
Data Source
AI summary
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.