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Bypass Patency and Amputation-Free Survival after Popliteal Aneurysm Exclusion Significantly Depends on Patient Age and Medical Complications: A Detailed Dual-Center Analysis of 395 Consecutive Elective and Emergency Procedures
Implementation of coupled acoustic-elastic solvers in the ExaHyPE2 hyperbolic PDE Engine
Intermolecular Enantioselective Amination Reactions Mediated by Visible Light and a Chiral Iron Porphyrin Complex
Guidelines for the creation of a data management plan
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EMDRIVE Architecture: Embedded Distributed Computing and Diagnostics from Sensor to Edge
Future automotive architectures are expected to transition
from a network-centric to a domain-centered architecture
featuring central compute units. Powerful domain controllers
or smart sensors alleviate the load on these central units and
communication systems. These controllers execute tasks with
varying criticalities on heterogeneous multicore processors, and
are ideally capable of dynamically balancing the computing load
between the central unit and sensors. Here, Artificial Intelligence
(AI) capabilities play a crucial role, as it is in high demand for such
an automotive architecture. However, AI still requires specialized
accelerators to improve their computation performance.
Task-oriented distributed computing with criticalities up to
ASIL-D necessitates the development and utilization of specialized
methodologies, such as safety, through the isolation and abstraction
of low-level hardware concepts. Meanwhile, online monitoring
and diagnostics become vital features to detect errors during
operation.
The EMDRIVE architecture includes methods, components,
and strategies to enhance the performance, safety, and security
of such distributed computing platforms. The nationally funded
EMDRIVE project connects its twelve partners from academia
and industry and is currently in its intermediate stage