论文标题
XRBENCH：扩展现实（XR）机器学习基准套件
XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse
论文作者
论文摘要
实时多任务多模型（MTMM）工作负载是一种深度学习推理工作负载的一种新形式，正在为应用程序（例如Extended Reality（XR））等应用领域提供支持，以支持元用例。这些工作负载将用户交互与计算复杂的机器学习（ML）活动相结合。与标准ML应用相比，这些ML工作负载带来了独特的困难和约束。实时MTMM工作负载对未来的ML系统和设备施加异质性和并发要求，因此需要开发新功能。本文首先讨论了这些实时MTMM ML工作负载的各种特征，并提出了评估XR系统未来ML硬件的性能的本体。接下来，我们介绍XRBENCH，这是MTMM ML任务，模型和用法方案的集合，这些方案以三种代表性的方式执行这些模型：cascaded，croment和cascaded-concurrent用于XR用例。最后，我们强调需要正确捕获需求的新指标。我们希望我们的工作能够刺激研究，并导致开发新一代用于XR用例的ML系统。 XRBENCH可作为开源项目提供：https：//github.com/xrbench
Real-time multi-task multi-model (MTMM) 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 MTMM 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 MTMM ML workloads and presents an ontology for evaluating the performance of future ML hardware for XR systems. Next, we present XRBENCH, a collection of MTMM ML tasks, models, and usage scenarios that execute these models in three representative ways: cascaded, concurrent, and cascaded-concurrent 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. XRBench is available as an open-source project: https://github.com/XRBench
