Towards Energy-Efficiency by Navigating the Trilemma of Energy, Latency, and Accuracy

TL;DR

This paper explores how to optimize energy efficiency in XR scene reconstruction by balancing energy, latency, and accuracy, achieving significant energy savings with minimal quality loss.

Contribution

It introduces a comprehensive co-optimization framework across algorithm, execution, and data levels to navigate the energy-latency-accuracy trade-off in XR scene reconstruction.

Findings

Up to 60X energy savings over baseline

Achieved 25X energy reduction with minimal quality loss

Identified Pareto-optimal design curve for trade-offs

Abstract

Extended Reality (XR) enables immersive experiences through untethered headsets but suffers from stringent battery and resource constraints. Energy-efficient design is crucial to ensure both longevity and high performance in XR devices. However, latency and accuracy are often prioritized over energy, leading to a gap in achieving energy efficiency. This paper examines scene reconstruction, a key building block for immersive XR experiences, and demonstrates how energy efficiency can be achieved by navigating the trilemma of energy, latency, and accuracy. We explore three classes of energy-oriented optimizations, covering the algorithm, execution, and data, that reveal a broad design space through configurable parameters. Our resulting 72 designs expose a wide range of latency and energy trade-offs, with a smaller range of accuracy loss. We identify a Pareto-optimal curve and show that the designs on the curve are achievable only through synergistic co-optimization of all three optimization classes and by considering the latency and accuracy needs of downstream scene reconstruction consumers. Our analysis covering various use cases and measurements on an embedded class system shows that, relative to the baseline, our designs offer energy benefits of up to 60X with potential latency range of 4X slowdown to 2X speedup. Detailed exploration of a use case across representative data sequences from ScanNet showed about 25X energy savings with 1.5X latency reduction and negligible reconstruction quality loss.