The 22nd International Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2021)
Session Keynote-Industry
Industry Keynotes
Conference
8:30 AM
—
10:00 AM CST
Local
Jul 27 Tue, 8:30 PM
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10:00 PM EDT
Industry Keynotes: Is network ready for video ubiquity? An outlook from Alibaba's video services
Yunfei Ma (Alibaba Group)
0
The COVID19 pandemic has profoundly changed the landscape of videos and redefined how we watch, what we watch and why we watch. For example, hundreds of millions of users watch videos on Taobao to decide what to buy, collaborate with colleagues remotely via video conferencing on Dingtalk, enjoy precious leisure minute with Youku, and receive education in streaming classroom powered by AliCloud. These revolutions put new pressures and requirements on the network in terms of bandwidth, latency, scalability, and reliability. However, there is a big gap. The truth is, today's network is far from being the "ideal pipe", and we suffer, more or less, from the agony of unstable, slow, and failed network connectivity. In this talk, I will discuss the lessons we learned from Alibaba video services over the past two years and the insights we gained to close this gap. Finally, I will discuss WAVE, a wireless and video entanglement framework, how WAVE addresses these challenges, and the opportunities in the future path.
Bio:
Yunfei is a lead researcher at XG Lab in the Alibaba Damo Academy and he is now responsible for building the next generation mobile transport protocols and algorithms. At Alibaba, he and his colleagues developed XLINK, the first QoE-driven multi-path QUIC transport protocol, and NFC+, the world�s longest NFC communication system. Before joining Alibaba, he was a postdoctoral researcher at MIT Media Lab. He received Ph.D. in Electrical and Computer Engineering from Cornell University and B.S. from USTC. He has published more than 10 papers on top conferences including SIGCOMM, MOBICOM and NSDI and he holds more than 15 granted US patents. His research has been covered by media outlets including BBC, The Verge, MIT Technology Review, the CBS Morning and IEEE Spectrum. His work NFC+ has been named the top ten RFID breakthroughs in the year of 2020.
Bio:
Yunfei is a lead researcher at XG Lab in the Alibaba Damo Academy and he is now responsible for building the next generation mobile transport protocols and algorithms. At Alibaba, he and his colleagues developed XLINK, the first QoE-driven multi-path QUIC transport protocol, and NFC+, the world�s longest NFC communication system. Before joining Alibaba, he was a postdoctoral researcher at MIT Media Lab. He received Ph.D. in Electrical and Computer Engineering from Cornell University and B.S. from USTC. He has published more than 10 papers on top conferences including SIGCOMM, MOBICOM and NSDI and he holds more than 15 granted US patents. His research has been covered by media outlets including BBC, The Verge, MIT Technology Review, the CBS Morning and IEEE Spectrum. His work NFC+ has been named the top ten RFID breakthroughs in the year of 2020.
Industry Keynotes: Empower Intelligent Edge Computing: Opportunities and Challenges
Ting Cao (Microsoft Research Asia)
0
The upheaval of AI applications has spawned a great many innovations across the whole stack, including the design of neural network (NN) accelerators, runtime frameworks, novel NN algorithms, and diverse scenario-specific solutions. The active use and interaction of these innovations on edge computing, which has strict requirements on latency and energy consumption, have raised unique challenges and opportunities compared to the cloud-side counterpart.
This talk will center on these challenges and opportunities, and introduce the works of our team towards empowering intelligent edge computing. The talk will cover our projects on the edge NN-runtime implementation which fully consider the features of heterogeneous edge hardware to gain substantial speedup, the hardware-aware NN algorithm design to achieve real efficiency on diverse edge NN deployment platforms, and the end-to-end solutions for specific edge AI applications.
This talk will center on these challenges and opportunities, and introduce the works of our team towards empowering intelligent edge computing. The talk will cover our projects on the edge NN-runtime implementation which fully consider the features of heterogeneous edge hardware to gain substantial speedup, the hardware-aware NN algorithm design to achieve real efficiency on diverse edge NN deployment platforms, and the end-to-end solutions for specific edge AI applications.
Session Chair
Yunxin Liu (THU)
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