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

ISBN-13: 9783031218668 / Angielski / Miękka / 2022 / 287 str.

Martin Schulz; Carsten Trinitis; Nikela Papadopoulou

Opis

This book constitutes the proceedings of the 35^th 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.

Spis treści

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.