Full System Architecture Modeling for Wearable Egocentric Contextual AI

2025-12-17

AI Summary

Designing all-weather wearable context-aware AI systems faces dual challenges of stringent power constraints and high system complexity. Method: We propose the first full-system power co-modeling framework—built upon the Aria2 platform—that transcends isolated component optimization. By integrating egocentric vision perception, dynamic context modeling, low-power heterogeneous computing, and power–performance co-analysis, we establish an end-to-end system architecture model. Contribution/Results: Our analysis reveals the absence of a dominant power-consuming component, underscoring the necessity of cross-layer joint optimization. Experimental validation confirms that a holistic system-level perspective is critical for achieving extended battery life. We derive three fundamental architectural design principles. This work establishes a scalable, foundational architecture for practical wearable generative AI assistants.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The next generation of human-oriented computing will require always-on, spatially-aware wearable devices to capture egocentric vision and functional primitives (e.g., Where am I? What am I looking at?, etc.). These devices will sense an egocentric view of the world around us to observe all human- relevant signals across space and time to construct and maintain a user's personal context. This personal context, combined with advanced generative AI, will unlock a powerful new generation of contextual AI personal assistants and applications. However, designing a wearable system to support contextual AI is a daunting task because of the system's complexity and stringent power constraints due to weight and battery restrictions. To understand how to guide design for such systems, this work provides the first complete system architecture view of one such wearable contextual AI system (Aria2), along with the lessons we have learned through the system modeling and design space exploration process. We show that an end-to-end full system model view of such systems is vitally important, as no single component or category overwhelmingly dominates system power. This means long-range design decisions and power optimizations need to be made in the full system context to avoid running into limits caused by other system bottlenecks (i.e., Amdahl's law as applied to power) or as bottlenecks change. Finally, we reflect on lessons and insights for the road ahead, which will be important toward eventually enabling all-day, wearable, contextual AI systems.

Problem

• Designing wearable systems for contextual AI with power constraints
• Providing a complete system architecture model for wearable AI
• Optimizing system-wide power to avoid bottlenecks in wearable AI

Innovation

• Full system architecture modeling for wearable AI
• End-to-end power optimization across all components
• Design space exploration to avoid system bottlenecks