ARCS 2022: INTERNATIONAL CONFERENCE ON ARCHITECTURE OF COMPUTING SYSTEMS

This page contains an index consisting of author-provided keywords.

A
Active Learning	A context aware and self-improving monitoring system for field vegetables
Anomaly Detection	NDNET: a Unified Framework for Anomaly and Novelty Detection
approximate computing	Effects of Approximate Computing on Workload Characteristics
Architecture	SpMV: An embarrassing kernel for modern compute devices
Artificial DNA (ADNA)	Organic Computing to Improve the Dependability of an Automotive Environment
Artificial Hormone System (AHS)	Organic Computing to Improve the Dependability of an Automotive Environment
Autonomous Driving Vehicle	Organic Computing to Improve the Dependability of an Automotive Environment
B
benchmarks	Effects of Approximate Computing on Workload Characteristics
Bitstream	Using Look Up Table Content as Signatures to Identify IP Cores in Modern FPGAs
C
Cabbage	A context aware and self-improving monitoring system for field vegetables
Co-Design	QPU-System Co-Design for Quantum HPC Accelerators
Co-Scheduling	Orchestrated Co-Scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
Coarse-Grained Reconfigurable Array	Pasithea-1: An Energy-Efficient Self-Contained CGRA With RISC-Like ISA
Computational accelerators	QPU-System Co-Design for Quantum HPC Accelerators
Configuration Memory	Memristor based FPGAs: Understanding the Effect of Configuration Memory Faults
CPU-GPU Heterogeneous Systems	Orchestrated Co-Scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
D
Deep Neural Networks	FPGA-based Dynamic Deep Learning Acceleration for Real-time Video Analytics
Deep Reinforcement Learning	Deep Reinforcement Learning with a Classifier System – First Steps
Defect Rate	Memristor based FPGAs: Understanding the Effect of Configuration Memory Faults
DVFS	GAE-LCT: A run-time GA-based Classifier Evolution Method for Hardware LCT controlled SoC Performance-Power Optimization
E
Edge Computing	FPGA-based Dynamic Deep Learning Acceleration for Real-time Video Analytics Hardware Isolation Support for Low-Cost SoC-FPGAs
Energy Awareness	Energy Efficient Frequency Scaling on GPUs in Heterogeneous HPC Systems
Energy-Efficient Computing	Pasithea-1: An Energy-Efficient Self-Contained CGRA With RISC-Like ISA
Evolutionary Reinforcement Learning	Deep Reinforcement Learning with a Classifier System – First Steps
F
Fault Diagnosis	Organic Computing to Improve the Dependability of an Automotive Environment
FPGA	Memristor based FPGAs: Understanding the Effect of Configuration Memory Faults
FPGA Architecture	Using Look Up Table Content as Signatures to Identify IP Cores in Modern FPGAs
FPGAs	FPGA-based Dynamic Deep Learning Acceleration for Real-time Video Analytics
G
Genetic Algorithm	GAE-LCT: A run-time GA-based Classifier Evolution Method for Hardware LCT controlled SoC Performance-Power Optimization
H
Hardware Accelerator	FPGA-based Dynamic Deep Learning Acceleration for Real-time Video Analytics
Hardware/Software Co-Design	Hardware Isolation Support for Low-Cost SoC-FPGAs
Heterogeneous Computing	Energy Efficient Frequency Scaling on GPUs in Heterogeneous HPC Systems
High Performance Computing	Energy Efficient Frequency Scaling on GPUs in Heterogeneous HPC Systems
Hypervisor	Hardware Isolation Support for Low-Cost SoC-FPGAs
I
Instance Segmentation	A context aware and self-improving monitoring system for field vegetables
instruction level parallelism	Dual-IS: Instruction Set Modality for Efficient Instruction Level Parallelism
Instruction Set Architecture	Pasithea-1: An Energy-Efficient Self-Contained CGRA With RISC-Like ISA
Interpolated Experience Replay	Semi-Model-Based Reinforcement Learning in Organic Computing Systems
IP Core	Using Look Up Table Content as Signatures to Identify IP Cores in Modern FPGAs
L
Learning Classifier Systems	Deep Reinforcement Learning with a Classifier System – First Steps
Learning Classifier Table	GAE-LCT: A run-time GA-based Classifier Evolution Method for Hardware LCT controlled SoC Performance-Power Optimization
Library	NDNET: a Unified Framework for Anomaly and Novelty Detection
M
Machine Learning	NDNET: a Unified Framework for Anomaly and Novelty Detection Orchestrated Co-Scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
Many-Core	Low-level Fun with Parallel Runtime Systems
Many-Core Systems
Memristor	Memristor based FPGAs: Understanding the Effect of Configuration Memory Faults
microcode	Dual-IS: Instruction Set Modality for Efficient Instruction Level Parallelism
Mixed-Criticality Systems	Hardware Isolation Support for Low-Cost SoC-FPGAs
Model-Based	Semi-Model-Based Reinforcement Learning in Organic Computing Systems
Model-Free	Semi-Model-Based Reinforcement Learning in Organic Computing Systems
Model-free control	GAE-LCT: A run-time GA-based Classifier Evolution Method for Hardware LCT controlled SoC Performance-Power Optimization
Multi-Core	Low-level Fun with Parallel Runtime Systems
Multi-core SoCs	Hardware Isolation Support for Low-Cost SoC-FPGAs
multiple instruction set architecture	Dual-IS: Instruction Set Modality for Efficient Instruction Level Parallelism
N
Novelty Detection	NDNET: a Unified Framework for Anomaly and Novelty Detection
Numerical Simulation	QPU-System Co-Design for Quantum HPC Accelerators
O
OpenMP	Low-level Fun with Parallel Runtime Systems
operating system	Protected Functions: User Space Privileged Function Calls
Organic Computing	A context aware and self-improving monitoring system for field vegetables Organic Computing to Improve the Dependability of an Automotive Environment Semi-Model-Based Reinforcement Learning in Organic Computing Systems Deep Reinforcement Learning with a Classifier System – First Steps
P
Parallel Programming
Power Capping	Orchestrated Co-Scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
privilege separation	Protected Functions: User Space Privileged Function Calls
Programming Models	Low-level Fun with Parallel Runtime Systems
protection rings	Protected Functions: User Space Privileged Function Calls
Q
Quantum computing	QPU-System Co-Design for Quantum HPC Accelerators
R
Radiation Effect	On the Reliability of Real-time Operating System on Embedded Soft Processor for Space Applications
Real-time Operating System	On the Reliability of Real-time Operating System on Embedded Soft Processor for Space Applications
Real-time video analytics	FPGA-based Dynamic Deep Learning Acceleration for Real-time Video Analytics
Reconfigurable Computing	Pasithea-1: An Energy-Efficient Self-Contained CGRA With RISC-Like ISA
Reinforcement Learning	GAE-LCT: A run-time GA-based Classifier Evolution Method for Hardware LCT controlled SoC Performance-Power Optimization Semi-Model-Based Reinforcement Learning in Organic Computing Systems
Resource Partitioning	Orchestrated Co-Scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
Run-time management	GAE-LCT: A run-time GA-based Classifier Evolution Method for Hardware LCT controlled SoC Performance-Power Optimization
S
Smart Farming	A context aware and self-improving monitoring system for field vegetables
Soft Processor	On the Reliability of Real-time Operating System on Embedded Soft Processor for Space Applications
Space Application	On the Reliability of Real-time Operating System on Embedded Soft Processor for Space Applications
Sparse Matrix-Vector Multiplication	SpMV: An embarrassing kernel for modern compute devices
Spatial Isolation	Hardware Isolation Support for Low-Cost SoC-FPGAs
SpMV	SpMV: An embarrassing kernel for modern compute devices
System Architecture	QPU-System Co-Design for Quantum HPC Accelerators
T
Testsystem	Organic Computing to Improve the Dependability of an Automotive Environment
transport triggered architecture	Dual-IS: Instruction Set Modality for Efficient Instruction Level Parallelism
U
Unsupervised Learning	NDNET: a Unified Framework for Anomaly and Novelty Detection
user-space protection	Protected Functions: User Space Privileged Function Calls
W
workload characteristics	Effects of Approximate Computing on Workload Characteristics
X
XCS	Deep Reinforcement Learning with a Classifier System – First Steps

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