Titlebook: Architecture of Computing Systems; 37th International C Dietmar Fey,Benno Stabernack,Thilo Pionteck Conference proceedings 2024 The Editor(

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An Organic Computing Approach for?CARLA Simulators the likelihood of failures and unforeseen errors. To address these challenges, this article presents the integration of the . (ADNA)-based . (OC) approach into the . (CARLA) simulator. CARLA is a powerful tool for the automotive industry to explore autonomous driving in a cost-efficient way. It th

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Idle is the?New Sleep: Configuration-Aware Alternative to?Powering Off FPGA-Based DL Accelerators Duerogeneous platforms, aligning with the principles of sustainable computing. Instead of focusing on the inference phase, we introduce innovative optimizations to minimize the overhead of the FPGA configuration phase. By fine-tuning configuration parameters correctly, we achieved a 40.13-fold reducti

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On-the-Fly CT Image Pre-processing on?MPSoC-FPGAseffective diagnosis and treatments. To support tumor ablation procedures, CT scanners must pre-process 2D projections and reconstruct 3D slices of the human body in real time, while data are acquired. This paper proposes a lightweight processing architecture for MPSoC-FPGA that performs the “CT pre-

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AccProf: Increasing the?Accuracy of?Embedded Application Profiling Using FPGAsant for embedded systems that operate under fixed timing constraints, which if not met, could lead to system failure. Existing profiling solutions targeting embedded systems introduce an overhead to the running application that distorts the collected profiling data. This paper proposes AccProf for S

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Halophytes: An Integrative Anatomical Studyocessors are examined regarding AT efficiency. The proposed versatile architecture turns out to be up to 1800x more area efficient than a RISC-V processor, thereby playing a vital role in accelerating neuroscience simulation and research in AI.

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https://doi.org/10.1007/978-3-540-48722-7uggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based