Modern automotive-grade embedded computing platforms feature high-performance
Graphics Processing Units (GPUs) to support the massively parallel processing power …

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …

GPUs are widely adopted in HPC and cloud computing platforms to accelerate general-
purpose workloads. However, modern GPUs do not support flexible preemption, leading to …

Recent commercial hardware platforms for embedded real-time systems feature
heterogeneous processing units and computing accelerators on the same System-on-Chip …

Graphics Processing Units (GPU) are increasingly deployed on Cyber-physical Systems
(CPSs), frequently used to perform real-time safety-critical functions, such as object …

Heterogeneous multi-core architectures consisting of CPUs and GPUs are commonplace in
today's embedded systems. These architectures offer potential for energy efficient computing …

Multi-stage user-facing applications on GPUs are widely-used nowa-days, and are often
implemented to be microservices. Prior re-search works are not applicable to ensuring QoS …

Many applications such as autonomous driving and augmented reality, require the
concurrent running of multiple deep neural networks (DNN) that poses different levels of real …

Motivated by the popularity of edge intelligence, DNN services have been widely deployed
at the edge, posing significant performance pressure on edge servers. How to improve the …

Multi-application execution in Graphics Processing Units (GPUs), a promising way to utilize
GPU resources, is still challenging. Some pieces of prior work (eg, spatial multitasking) have …