Keynotes

The conference will include keynote talks by distinguished speakers from industry and academia.

In the past decade, there has been significant progress in private space development, commonly referred to as NewSpace. One of the most notable advancements is the deployment of satellite constellations, with numerous small satellites being placed in low Earth orbit (LEO) to support communication and remote sensing applications. Looking ahead, it is anticipated that CPS (Cyber-Physical Systems) will be extended into space by leveraging artificial satellites as IoT devices or NTN communication platforms. To achieve this vision, we are conducting fundamental research on high-bandwidth communication satellites utilizing formation flying techniques. This presentation will introduce these emerging trends and highlight the related research activities.

Large-scale distributed machine learning training networks are increasingly facing scaling problems with respect to FLOPS per deployed compute node. Communication bottlenecks can inhibit the effective utilization of expensive GPU resources. The root cause of these performance problems is not insufficient transmission speed or slow servers; it is the structure of the distributed computing and the communication characteristics it incurs. Large machine learning workloads typically provide relatively asymmetric, and sometimes
centralized, communication structures, such as gradient aggregation and model update distribution.  Even when training networks are less centralized, the amount of data that needs to be sent to aggregate several thousand input values through collective communication functions such as AllReduce can lead to Incast problems that overload network resources and servers. This talk discusses opportunities and challenges for a systems approach towards making distributed machine learner faster, more energy-efficient, and scalable.