Self-driving systems execute an ensemble of different self-driving workloads on embedded
systems in an end-to-end manner, subject to functional and performance requirements. To …

The increasing computing demands of autonomous driving applications have driven the
adoption of multicore processors in real-time systems, which in turn renders energy …

Latency-critical applications, eg, automated and assisted driving services, can now be
deployed in fog or edge computing environments, offloading energy-consuming tasks from …

This paper summarizes the talks of a special session on the IPF 2.0 project, a collaborative
German-US research project that leverages self-awareness principles for the self …

Runtime energy management has become quintessential for multi-sensor autonomous
systems at the edge for achieving high performance given the platform constraints. Typical …

The lifespan of autonomous vehicles is increasing, exposing them to aging and permanent
faults that can impact timing safety based on design-time analyses such as worst-case …

This paper investigates the challenges posed by delays in Closed-Loop Sense-Act Systems
in the context of Adversarial Internet of Things (IoT) applications. Prior work focused on …

An autonomous machine operates on its own by continuously sensing the environment,
making decisions, and executing actions. Due to this attribute, it is well-suited for managing …

The ever-increasing demands of data-intensive applications and the rapid evolution of
computer architectures have posed significant challenges in memory performance and …

최근 10 년간 뉴럴 네트워크 (Neural Network) 는 방대한 데이터에 힘입어 가장 널리 사용되는
인공 지능모델로 급부상하였다. 뉴럴 네트워크는 자연어 처리 (Natural Language Processing; …