Architecture of Computing Systems 36th International Conference, ARCS 2023, Athens, Greece, June 13–15, 2023, Proceedings

T his book constitutes the proceedings of the 36th International Conference on Architecture of Computing Systems, ARCS 2023, which took place in Athens, Greece, in June 2023.Back to top

Accelerating Neural Networks.- Energy Efficient LSTM Accelerators for Embedded FPGAs through Parameterised Architecture Design.- A Comparative Study of Neural Network Compilers on ARMv8 Architecture.- Organic Computing Methodology (OC).- A Decision-Theoretic Approach for Prioritzing Maintenance Activities in Organic Computing Systems.- Predicting Physical Disturbances in Organic Computing Systems using Automated Machine Learning.- Self-Adaptive Diagnosis and Reconfigurationin ADNA-Based Organic Computing.- Dependability and Fault Tolerance (VERFE) Error Codes in and for Network Steganography.- Modified Cross Parity Codes For Adjacent Double Error Correction.- Computer Architecture Co-Design.- COMPESCE: A Co-design Approach for memory subsystem Performance Analysis in HPC many-cores.- Post-Silicon Customization Using Deep Neural Networks.- Computer Architectures and Operating Systems.- TOSTING: Investigating Total Store Ordering on ARM.- Back to the Core-Memory Age: Running Operating Systems in NVRAM only.- Retrofitting AMD x86 processors with active virtual machine introspection capabilities.- Organic Computing Applications 1 (OC).- Abstract Artificial DNA’s Improved Time Bounds.- Evaluating the Comprehensive Adaptive Chameleon Middleware for Mixed-Critical Cyber-Physical Networks.- CoLeCTs: Cooperative Learning Classifier Tables for Resource Management in MPSoCs.- Hardware Acceleration.- Improved Condition Handling in CGRAs with Complex Loop Support.- FPGA-based Network-attached Accelerators – An Environmental Life Cycle Perspective.- Optimization of OLAP In-memory DB Management Systems with PIM.- Organic Computing Applications 2 (OC).- Real-Time Data Transmission Optimization on 5G Remote-Controlled Units using Deep Reinforcement Learning.- Autonomous ship collision avoidance trained on observational data.- Towards Dependable Unmanned Aerial Vehicle Swarms Using Organic Computing.