XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse

Abstract

Real-time multi-model multi-task (MMMT) workloads, a new form of deep learning inference workloads, are emerging for applications areas like extended reality (XR) to support metaverse use cases. These workloads combine user interactivity with computationally complex machine learning (ML) activities. Compared to standard ML applications, these ML workloads present unique difficulties and constraints. Real-time MMMT workloads impose heterogeneity and concurrency requirements on future ML systems and devices, necessitating the development of new capabilities. This paper begins with a discussion of the various characteristics of these real-time MMMT ML workloads and presents an ontology for evaluating the performance of future ML hardware for XR systems. Next, we present XRBENCH, a collection of MMMT ML tasks, models, and usage scenarios that execute these models in three representative ways: cascaded, concurrent, and cascaded-concurrency for XR use cases. Finally, we emphasize the need for new metrics that capture the requirements properly. We hope that our work will stimulate research and lead to the development of a new generation of ML systems for XR use cases.

Navigating the metaverse: A technical review of emerging virtual worlds

“…The metaverse's character interactions, behavior simulations, and content creation are all supported by artificial intelligence (AI) and machine learning (ML) algorithms (Herath & Mittal, 2022;Kwon et al, 2023). AI-driven avatars may move realistically and react intelligently to user interactions, adding to the immersion and realism of the experience.…”