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E-raamat: Architecture of Computing Systems: 35th International Conference, ARCS 2022, Heilbronn, Germany, September 13-15, 2022, Proceedings

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Architecture of Computing Systems: 35th International Conference, ARCS 2022, Heilbronn, Germany, September 13-15, 2022, ProceedingsSuurem pilt
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This book constitutes the proceedings of the 35th International Conference on Architecture of Computing Systems, ARCS 2022, held virtually in July 2022.





The 18 full papers in this volume were carefully reviewed and selected from 35 submissions.





ARCS provides a platform covering newly emerging and cross-cutting topics, such as autonomous and ubiquitous systems, reconfigurable computing and acceleration, neural networks and artificial intelligence. The selected papers cover a variety of topics from the ARCS core domains, including energy efficiency, applied machine learning, hardware and software system security, reliable and fault-tolerant systems and organic computing.
Energy Efficiency.- Energy Efficient Frequency Scaling on GPUs in
Heterogeneous HPC Systems.- Dual-IS: Instruction Set Modality for Efficient
Instruction Level Parallelism.- Pasithea-1: An Energy-Efficient
Self-Contained CGRA With RISC-Like ISA.- Applied Machine Learning.-
Orchestrated Co-Scheduling, Resource Partitioning, and Power Capping on
CPU-GPU Heterogeneous Systems via Machine Learning.- FPGA-based Dynamic Deep
Learning Acceleration for Real-time Video Analytics.- Advanced Computing
Techniques.- Effects of Approximate Computing on Workload Characteristics.-
QPU-System Co-Design for Quantum HPC Accelerators.- Hardware and Software
System Security.- Protected Functions: User Space Privileged Function Calls.-
Using Look Up Table Content as Signatures to Identify IP Cores in Modern
FPGAs.- Hardware Isolation Support for Low-Cost SoC-FPGAs.- Reliable and
Fault-tolerant systems.- Memristor based FPGAs: Understanding the Effect of
Configuration Memory Faults.- On the Reliability of Real-time Operating
System on Embedded Soft Processor for Space Applications.- Special Track:
Organic Computing.- NDNET: a Unified Framework for Anomaly and Novelty
Detection.- Organic Computing to Improve the Dependability of an Automotive
Environment.- A context aware and self-improving monitoring system for field
vegetables.- Semi-Model-Based Reinforcement Learning in Organic Computing
Systems.- Deep Reinforcement Learning with a Classifier System First
Steps.- GAE-LCT: A run-time GA-based Classifier Evolution Method for Hardware
LCT controlled SoC Performance-Power Optimization.
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