Design Automation of AI-Enabled Edge-Based Embedded and Cyber-Physical Systems

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.