Mohammad Abdullah Al Faruque

Cyber-physical systems (CPS), such as automotive, manufacturing, and power grid systems, are engineered systems built from and depend upon the seamless integration of computation and physical components. Embedded systems comprising of hardware and software systems are the primary enabling technology for these cyber-physical systems. Moreover, when cyber-physical systems get connected to the Internet, it forms the Internet-of-Things (IoT). Today, CPSs can be found in security-sensitive areas such as aerospace, automotive, energy, healthcare, manufacturing transportation, entertainment, and consumer appliances. Compared to the traditional information processing systems, due to the tight interactions between cyber and physical components in CPSs and closed-loop control from sensing to actuation, new vulnerabilities emerge from the boundaries between various layers and domains. In this seminar, Dr. Al Faruque will discuss how new vulnerabilities emerge at the intersection of multiple components and subsystems and their different hardware, software, and physical layers. Several recent examples from various cyber-physical systems (e.g., an automotive system) will be presented in this talk. To understand these new vulnerabilities, a very different set of methodologies and tools are needed. Defenses against these vulnerabilities also demand new hardware/software co-design approaches. The seminar will highlight recent developments in this regard. This seminar's primary goal will be to highlight various research challenges and the need for novel scientific solutions from the EDA research community and definitely from the larger embedded systems, cyber-physical systems, distributed computing systems, and computer architecture research communities.

Over the years, design automation methodologies have been employed to alleviate the extensive task of manual system design across the different levels of abstraction, as in the EDA, Embedded, and Cyber-Physical Systems (CPS) domains. As Deep Learning (DL) becomes the enabling technology of today's intelligent applications, design automation methodologies have found their way into the DL field through techniques like Neural Architecture Search (NAS). Such methods are providing high-performing neural architecture models compared to the manually designed ones. Yet, driven by the application requirements of real-time inference and computation efficiency, the current design tendency is to push the computation to the edge of the network itself, be it on the user's end devices or the network gateways. Consequently, device specifications, application characteristics, and computing paradigms must be integrated within the design automation methodologies. Therefore, sophisticated techniques are needed to capture the interacting dynamics of the different system components in terms of compute capabilities, resource utilization, and intra-system communication. In this seminar, Dr. Al Faruque will discuss how to implement design automation methodologies for DL applications, tuned to the different requirements of modern smart edge-AI systems. He will present several examples ranging from low-power wearable devices to compute-intensive Autonomous Vehicles. Moreover, this seminar will highlight how the emerging split computation and early-exit computing can be incorporated into the design optimization aspect to provide AI-enabled edge systems for different applications.

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.