Efficient Audio-Visual Understanding on AR Devices

Abstract

Augmented reality (AR) is a set of technologies that will fundamentally change the way we interact with our environment. It represents a merging of the physical and the digital worlds into a rich, context aware user interface delivered through a socially acceptable form factor such as eyeglasses. The majority of these novel experiences in AR systems will be powered by AI because of their superior ability to handle in-the-wild scenarios. A key AR use case is a personalized, proactive and context-aware Assistant that can understand the user’s activity and their environment using audio-visual understanding models. In this presentation, we will discuss the challenges and opportunities in both training and deployment of efficient audio-visual understanding on AR glasses. We will discuss enabling always-on experiences within a constrained power budget using cascaded multimodal models, and co-designing them with the target hardware platforms. We will present our early work to demonstrate the benefits and potential of such a co-design approach and discuss open research areas that are promising for the research community to explore.

Meng Li

Assistant Professor

I am currently a tenure-track assistant professor jointly affiliated with the Institute for Artificial Intelligence and School of Integrated Circuits in Peking University. My research interests focus on efficient and secure multi-modality AI acceleration algorithms and hardwares.