Work

ARIA: Optimizing Vision Foundation Model Inference on Heterogeneous Mobile Processors for Augmented Reality

Year: 2025

Type: conference-paper

Abstract: Mobile Augmented Reality (AR) applications demand high-quality, real-time visual prediction, including pixel-level depth and semantics, to enable immersive and context-aware user experiences. Recently, Vision Foundation Models (VFMs) offer strong generalization capabilities on diverse and unseen data, supporting scalable mobile AR experiences. However, deploying VFMs on mobile devices is challenging due to computational limitations, particularly in maintaining both prediction accuracy and real-time performance. In this paper, we present ARIA, the first system that enables on-device inference acceleration of a VFM. ARIA employs the heterogeneity of mobile processors through a parallel and selective inference scheme: full-frame prediction is periodically offloaded to a processor with high parallelism capability like GPU, while low-latency updates on dynamic regions are conducted via a specialized accelerator like NPU. Implemented and evaluated using mobile devices, ARIA achieved significant improvements in accuracy and deadline success rate on diverse real-world mobile AR scenarios.

Source: International Conference on Mobile Systems, Applications, and Services

DOI: 10.1145/3711875.3729161

Authors: Chanyoung Jung, Jeho Lee, Gunjoong Kim, Jiwon Kim, Seonghoon Park, Hojung Cha

Institutions: Yonsei University, Uppsala University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion

Awards: G2325587046, G7135983821