Architecture of Computing Systems

35th International Conference, ARCS 2022, Heilbronn, Germany, September 13–15, 2022, Proceedings

Häftad, Engelska, 2022

Av Martin Schulz, Carsten Trinitis, Nikela Papadopoulou, Thilo Pionteck

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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.

Produktinformation

Utgivningsdatum2022-12-14
Mått155 x 235 x 21 mm
Vikt478 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor287
FörlagSpringer International Publishing AG
ISBN9783031218668

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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.