The push towards fielding autonomous-driving capabilities in vehicles is happening at
breakneck speed. Semi-autonomous features are becoming increasingly common, and fully …

GPUs are being increasingly adopted as compute accelerators in many domains, spanning
environments from mobile systems to cloud computing. These systems are usually running …

Guaranteeing Quality-of-Service (QoS) of latency-sensitive applications while improving
server utilization through application co-location is important yet challenging in modern …

Autonomous vehicles are an exemplar for forward-looking safety-critical real-time systems
where significant computing capacity must be provided within strict size, weight, and power …

Model serving systems play a critical role in multiplexing machine learning inference jobs
across shared GPU infrastructure. These systems have traditionally sat at a high level of …

Abstract Graphical Processing Unit provides massive parallelism due to the presence of
hundreds of cores. Usage of GPUs for general purpose computation (GPGPU) has resulted …

As technology scales, GPUs are forecasted to incorporate an ever-increasing amount of
computing resources to support thread-level parallelism. But even with the best effort …

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

A fundamental shift is resha** how real-time analysis is applied in all forms of
autonomous 45 systems (eg, UAVs, robotics, and, especially, self-driving automobiles) …

Modern GPUs are broadly adopted in many multitasking environments, including data
centers and smartphones. However, the current support for the scheduling of multiple GPU …