Rising Star symposium Seminar by Changgang Zheng

Machine learning is widely used by data-intensive applications. However, standard accelerators struggle to handle the volume of data and meet low-latency requirements. In-network machine learning, offloading of machine learning tasks to run within the network devices, is an emerging solution to this problem. This presentation will introduce the concept of in-network machine learning, the technology and its implementation, including three general model mapping methodologies. As in-network machine learning is a
resource-constrained machine learning problem, two solutions will be introduced: a distributed deployment solution, and a hybrid deployment solution. The talk will explore a range of security-related applications of
in-network machine learning, including anomaly detection in a wide area network, traffic analysis at the IoT edge, bot detection and others. Finally, the talk will introduce a framework for rapid deployment of in-network machine learning on a range of targets, as well as several open-source projects for community use.