{"product_id":"9783032032805","title":"Architecture of Computing Systems : 38th International Conference, ARCS 2025, Kiel, Germany, April 22-24, 2025, Proceedings (Lecture Notes in Computer Science)","description":"\u003cp\u003eThis book constitutes the proceedings of the 38th International Conference on Architecture of Computing Systems, ARCS 2025, held in Kiel, Germany, in April 2025.\u003c\/p\u003e\u003cp\u003eThe 24 full papers presented together with 12 other papers in this volume were carefully reviewed and selected from 49 submissions. \u003c\/p\u003e\u003cp\u003eThese papers have been categorized in the following sections: self-powered embedded  systems up to plant powered high-performance systems, new emerging and cross-cutting topics,such as autonomous and ubiquitous systems, reconfigurable computing, mathematical models, neural networks, and artificial intelligence.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003e.- Main Track :\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Progressing Non-blocking Two-Sided Communication Using BlueField DPUs.\n.- Efficient Parallel Fuzzy Dilation for Visual Reasoning: Leveraging ARM CPUs and NVIDIA GPUs on Embedded Systems.\n.- Optimized Ternary Signed Digit Addition on Field Programmable Gate Arrays.\n.- FSST Compression of JSON Data on FPGAs.\n.- CHaOS: A Persistent Lightweight Cache Hybridization-aware OS.\n.- MxGPU: A Case Study on OS-Controlled GPGPU Multiplexing.\n.- Towards Complete Open-source Environments: FPGA-Based GPU Overlay Controlled by RISC-V.\n.- Revisiting Gradient Direction Algorithms in Electrostatic Placers.\n.- ZTL: Enabling Zoned Namespace Support for File Systems.\n.- Spend More to Save More (SM2): An Energy and Hardware-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization.\n.- OpenVADL: An Open Source Implementation of the Vienna Architecture Description Language.\n.- A SIMD MAC RISC-V Extension with Approximate Multipliers for Accelerating CNN Inference in Tiny Embedded Devices.\n.- Wildcat: Educational RISC-V Microprocessors.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- Special Track on Organic Computing (OC) :\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network.\n.- Unsupervised Anomaly Detection in Cellular Modem Metrics Using Deep Autoencoders.\n.- Hyperparameter Optimization for PSO-Based Energy-Aware Path Planning for AUV Swarms.\n.- Process-Level Simulation Testbed for Assessing Field Robot Swarms.\n.- Neural Rules for Reinforcement Learning with XCSF as Controller in Organic Computing Systems.\n.- Evaluating Adaptive Systems: A Comparative Study of XCS and Established Reinforcement Learning Algorithms in Noisy Multi-Step Environments.\n.- A Measurement Framework at Global and Local Levels for Hybrid Organic Computing Systems.\n.- Exploring Model Quantization in GenAI-based Image Inpainting and Detection of Arable Plants.\n.- Unify: Uncertainty incorporated Federated Learning for Object Detection.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- Special Track on Dependability and Fault Tolerance (VERFE):\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Extension of 2-bit error correcting BCH codes with simple decoding.\n.- Reduction of Average-Performance Degradation and Variance in Faulty CPU Cache Memories.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- PhD Forum :\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- A System Model for Flexible Multi-Objective Adaptation Planning in Hybrid Self-Adaptive and Self-Organizing Systems.\n.- Dynamic Multimodal Cyclist Behaviour Modelling: From Representation Insights to Federated Collaboration .\n.- Self-explanation of System Behaviour in Organic Computing Systems.\n.- Enhancing Dynamic Scene Understanding in Manual Assembly Processes.\n.- Situational Awareness by Audio Signals in Maritime Application .\n.- A Vision for Deep Reinforcement Learning with a Classi er System.\n.- AI-based computer vision methods to monitor emission-relevant parameters in livestock barns.\n.- Open the Black Box: Self-Explainability of AI in Autonomous Marine Systems.\n.- Leveraging Application-Speci c Knowledge for Energy-E cient Deep Learning Accelerators on Resource-Constrained FPGAs.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e.- Research Groups :\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e.- Presentation of the joint research project FORnanoSatellites.\n.- Feedback-guided Dataset Shaping for Automated Downstream Task Optimization.\n.- Disruptive Memory Technologies: An Overview of DFG Priority Program 2377.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default 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