Edge-native Intelligence and Micromachine Learning: Enabling a New Ecosystem of Large-scale Intelligent Computing Services
With the rapid advancement of large-scale neural networks and micromachine learning technologies, we are approaching a new frontier in the evolution of artificial intelligence. In this transition, we face comprehensive challenges in terms of "large-scale," "massive data," and "user diversity". It is crucial to fully utilize and balance computational resources and energy efficiency across networks to enable high-performance and cost-effective solutions. This calls for integrated designs that align "computing-power-network integration," "cloud-edge-device collaboration," and "intelligent empowerment."
This lecture introduces a forward-looking perspective on how the fusion of edge-native intelligence and micromachine learning can support large-scale intelligent computing. Specifically, it will explore new paradigms for intelligent services driven by massive data and real-time demands, and how to systematically design service infrastructures for such scenarios.
Date and Time
Location
Hosts
Registration
- Date: 30 May 2025
- Time: 08:20 AM UTC to 10:10 AM UTC
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- Southern University of Science and Technology
- Shenzhen, Guangdong
- China
- Building: Research Building 1
Speakers
Zhou Qihua
Biography:
Zhou Qihua, Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech). He received his Ph.D. from Hong Kong Polytechnic University and is a recipient of multiple national-level talent honors. His research focuses on edge intelligence and AI for smart sensing, covering topics such as edge-native intelligence, foundation models, micromachine learning, neural signal flow media, and more.
He has led or participated in several national research projects and has published multiple papers in top-tier CCF-A and Chinese Academy of Sciences Class I journals. He received the Best Paper Award from IEEE Transactions on Computers 2021, and authored the monograph Machine Learning on Commodity Tiny Devices: Theory and Practice. He is a regular reviewer for top international conferences and journals, and has received awards including the First Prize in the People's Daily Innovation Contest, recognition from China Construction Bank Medical Innovation, and multiple regional honors.
Personal homepage: https://qihuazhou.github.io