Research Topics

Systems Support for Intelligent Applications

This topic focuses on addressing the predictability, efficiency, and robustness challenges raised by emerging real-time intelligent applications such as self-driving cars and robotics. For instance, we develop new scheduling algorithms and runtime mechanisms that leverage the application-level end-to-end requirements of intelligent systems, and software frameworks that support the concurrent execution of multiple DNN tasks with deterministic and provable timing guarantees on heterogeneous hardware platforms.

Predictable Parallel Real-Time Systems

In this topic, we aim to build predictable systems that can take advantage of modern parallel hardware platforms (e.g., multi-core CPUs and GPUs), which are imperative to the efficiency and usability of mission-critical applications such as avionics and automotive systems. In particular, our focus has been on enabling predictable systems from unpredictable commercial off-the-shelf hardware components as doing so greatly reduces development cost and allows for timely and wide acceptance of new technology.

Smart Sensing and Processing in Dynamic Environment

For the robust and continuous operation of Cyber-Physical Systems (CPS) and the Internet of Things (IoT), a system must be able to deal with uncertainties under various environmental conditions. We have particularly focused on the dynamically changing availability of resources due to extreme thermal conditions and the lack of a reliable power source.

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