Researchers Xinyu Shi, Simei Yang, and Francky Catthoor from the IMEC research center in Belgium have published a study that delves into the architectural challenges posed by the increasing diversity of extended reality (XR) workloads, which include augmented and virtual reality applications. Their work aims to provide a systematic understanding of XR workloads and offer practical design guidelines for future XR systems-on-chips (SoCs). The research was published in the IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED).
The study highlights that edge and mobile platforms for XR must deliver ultra-low-latency performance under strict power and area constraints. However, the rapid increase in diverse XR workloads, characterized by heterogeneous operator types and complex dataflow structures, poses significant challenges to conventional accelerator architectures. These architectures are typically centered around convolutional neural networks (CNNs) and are becoming less effective with traditional compute-centric optimization strategies.
To address this, the researchers present an architectural classification of XR workloads using a cross-layer methodology. This approach integrates model-based high-level design space exploration with empirical profiling on commercial GPU and CPU hardware. By analyzing a representative set of workloads spanning 12 distinct XR kernels, the team distills the complex architectural characteristics into a small set of cross-layer workload archetypes, such as capacity-limited and overhead-sensitive.
Building on these archetypes, the researchers extract key architectural insights and provide actionable design guidelines for next-generation XR SoCs. They emphasize that future XR architecture design must shift from generic resource scaling toward phase-aware scheduling and elastic resource allocation. This shift is crucial for achieving greater energy efficiency and high performance in future XR systems.
For the energy sector, the implications of this research are significant. As XR technologies become more prevalent in industries such as energy management, remote monitoring, and training, the need for energy-efficient and high-performance XR systems will grow. The design guidelines provided by this study can help developers create more efficient XR devices, reducing energy consumption and improving overall performance. This, in turn, can lead to more sustainable and effective use of XR technologies in the energy industry.
This article is based on research available at arXiv.