image: Prof Yang CHAI, Associate Dean (Research) of the Faculty of Science and Professor of the Department of Applied Physics at PolyU, has been bestowed as the top ten winners of the prestigious Falling Walls Science Breakthroughs Award.
Credit: © 2024 Research and Innovation Office, The Hong Kong Polytechnic University. All Rights Reserved.
Prof Yang CHAI, Associate Dean (Research) of the Faculty of Science and Professor of the Department of Applied Physics at The Hong Kong Polytechnic University (PolyU), has been bestowed as the top ten winners of the prestigious Falling Walls Science Breakthroughs Award. This is a recognition of his groundbreaking research on sensory artificial intelligence (AI), which has paved the way for more energy-efficient, low-latency, and memory-optimised systems, enhancing for diverse applications such as mobile devices, IoT sensors and edge computing.
Prof CHAI has been named the 2024 Falling Walls Winner in the Engineering & Technology category for “Breaking the Wall of Efficient Sensory AI Systems”. He has developed novel hardware architectures and optimisation techniques, enabling the deployment of advanced sensory AI systems in mobile devices, IoT sensors, and edge computing, subsequently transforming applications in smart cities, autonomous vehicles, and industrial automation.
Overcoming crucial barriers in power consumption, latency and memory within sensory AI systems, Prof CHAI’s innovations unleash the potential of sensory AI across diverse industries and domains. Furthermore, the in-sensor computing strategy has sparked progress in improving decision-making and situational awareness, strengthening privacy and security, and transforming intelligent automation.
The Falling Walls Science Breakthroughs of the Year Award was initiated by the Berlin-based Falling Walls Foundation, to nominate the latest breakthroughs and outstanding science projects worldwide. This year, the high-level jury comprising globally recognised experts in the fields reviewed over 1,000 entries from 52 countries. In the Engineering & Technology category, 10 excellent winners were selected and shortlisted for the Science Breakthrough of the Year 2024 title.
Prof CHAI said, “The proliferation of data from ubiquitously distributed sensors leads to massive increases in sensory terminals. It is crucial to partially shift computation tasks to the sensory terminals. This transition substantially compresses the collected information and extracts key information, especially for sensor-rich platforms.”
Prof CHAI’s research clearly defines near-sensor concepts and in-sensor computing paradigms based on the physical distance between sensory and computing units. This classification further divides functions into low-level and high-level processing. His study explores the implementation of near-/in-sensor computing for different physical sensing systems and provide possible solutions for integrating sensing and processing units through advanced manufacturing technologies.
While Prof CHAI and his team focus on advancing computational hardware for sensory AI systems, the extraordinary capabilities of natural bioinspired sensory systems are a vital research inspiration.
By emulating human visual adaptability, which allows accurate object identification under various lighting conditions, the new bioinspired sensors developed by Prof CHAI’s team offer a solution by directly adapting to different light intensities. This approach avoids relying solely on backend computation, which emulates and even surpasses the human retina’s ability to adapt to various lighting levels.
The sensors reduce hardware complexity, boost image contrast in varied lighting conditions, thus improving machine vision systems for visual analysis and identification tasks. The work on bioinspired in-sensor vision adaptation was recognised as one of the Top 10 Hong Kong Innovation Technology News in 2022.
Inspired by flying insects’ high flicker function frequency (FFF), Prof CHAI has pioneered research on optoelectronic graded neurons for perceiving dynamic motion. This innovation efficiently encodes temporal information at sensory terminals, reducing the transfer of abundant vision data of fusing spatiotemporal (spatial and temporal) information in a computation unit. This advances machine vision systems with minimal hardware resource, promising potential applications in autonomous vehicles and surveillance systems.
These outstanding findings have been published in high-impact journals such as Nature Electronics, Nature Nanotechnology, and have been highlighted in Nature, IEEE Spectrum, and more and are highly cited by research teams worldwide.
Prof CHAI envisions, “My long-term goal is to develop cutting-edge microelectronic and nanoelectronic devices with new functionalities and unprecedented performance. Specifically, we intend to create imaging technology capable of perceiving three-dimensional (3D) depth, four-dimensional (4D) spatial-temporal and multiple spectral (beyond visible light) information. To achieve this, a bioinspired mechanism will be utilised to reduce power consumption and latency.”
Method of Research
Experimental study
Subject of Research
People