Speaker:

Dr. Niloofar Didar, Wayne State University

Abstract

Current Mobile Augmented Reality (MAR) apps incorporate resource-intensive tasks, including rendering high-quality virtual objects (AR tasks) and performing AI model inference (AI tasks) to analyze the environment. However, these platforms still lack the required onboard computational resources, posing challenges for delivering a seamless user experience. Furthermore, the incorporation of AI capabilities results in resource usage imbalances between AI and AR tasks. This is primarily due to the concurrent utilization of device hardware resources, such as the CPU and GPU by both task types, which negatively impacts overall system performance, leading to either increased AI inference time, or a poor quality of virtual objects. Previous research has focused on addressing concurrent resource utilization in AI tasks through dynamic task reallocation. However, these approaches have led to limited enhancements in average AI inference time primarily because they do not take into account optimizations for AR rendering tasks and their influence on system performance. Some research has addressed the limited computing power of mobile devices by utilizing cloud/edge computing resources, particularly when edge servers can offer better end-to-end latency. However, current task allocation methods for edge computing offloading do not account for the unique resource needs of individual tasks or explore the potential for data sharing among them. In this seminar, we address the challenges of system performance in MAR apps by leveraging on-device techniques. Our contribution involves the development of comprehensive frameworks for MAR apps that aim to balance the quality of virtual objects and the inference time of on-device AI tasks.

Biography

Niloofar holds a Ph.D. in Computer Science from Wayne State University. Throughout her doctoral studies, she focused on Improving power management and performance efficiency in mobile augmented reality (MAR) applications. Her research centered on optimizing on-device and edge resource utilization for machine learning and rendering tasks in MAR apps, with the goal of improving user experience, reducing power consumption, and enhancing task performance. Niloofar's research interests encompass a diverse range of areas, including autonomous systems, human-computer interaction, edge computing, Internet of Things (IoT), mobile computing, and smart cities.