Machine Learning on Graphs for Embedded and Cyber-Physical Systems

Embedded systems comprising hardware and software systems are the primary enabling technology for modern cyber-physical systems such as home devices, autonomous vehicles, and industrial manufacturing machines. The global embedded system market is estimated to grow from 86 billion dollars to 116 billion dollars by 2025. This phenomenon also reveals that these systems can now or in the future generate a considerable amount of data, so making good use of these data has become very important in this field. Conventional machine learning only applies to the data in the Euclidean form, while in practice, more and more data appears as non-Euclidean data such as graphs, manifolds, etc. To use these ML models, researchers have to manually engineer features from these non-Euclidean data with their expertise in the field and spend efforts in tuning them to avoid the loss of information from original data. In this case, Graph Learning techniques have become increasingly important as they can bypass this step and directly process the graph data. In this seminar, Dr. Al Faruque will discuss how to apply graph learning techniques to different applications for embedded systems. This talk will mainly present the approaches leveraging graph learning for Autonomous Driving Systems (ADSs), hardware security, and software security. In ADSs, recent work by Dr. Al Faruque leverages scene-graph as an intermediate representation to better understand the driving scenes and infer the level of riskiness for actions. In hardware security, Dr. Al Faruque’s group utilizes graph representations of hardware designs, such as abstract syntax trees (ASTs) or control-flow graphs (CFGs), and graph learning approaches to solve hardware security problems (hardware trojan detection/IP piracy detection) at the behavioral level.