Full System Architecture Modeling for Wearable Egocentric Contextual AI

TL;DR

This paper presents a comprehensive system architecture model for wearable egocentric contextual AI devices, emphasizing the importance of holistic design and power optimization across all components to enable practical, always-on personal AI assistants.

Contribution

It provides the first complete system architecture view of wearable contextual AI systems and insights into system-level design and power management strategies.

Findings

No single component dominates power consumption.

Holistic system modeling is crucial for effective power optimization.

Design decisions must consider system-wide bottlenecks and trade-offs.

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.