Architecture of Computing Systems [[electronic resource] ] : 36th International Conference, ARCS 2023, Athens, Greece, June 13–15, 2023, Proceedings / / edited by Georgios Goumas, Sven Tomforde, Jürgen Brehm, Stefan Wildermann, Thilo Pionteck |
Autore | Goumas Georgios |
Edizione | [1st ed. 2023.] |
Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
Descrizione fisica | 1 online resource (333 pages) |
Disciplina | 004.6 |
Altri autori (Persone) |
TomfordeSven
BrehmJürgen WildermannStefan PionteckThilo |
Collana | Lecture Notes in Computer Science |
Soggetto topico |
Computer networks
Computer systems Computer engineering Artificial intelligence Computers Computer Communication Networks Computer System Implementation Computer Engineering and Networks Artificial Intelligence Computer Hardware |
ISBN | 3-031-42785-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | 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. |
Record Nr. | UNISA-996546850003316 |
Goumas Georgios | ||
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
|
Architecture of Computing Systems : 36th International Conference, ARCS 2023, Athens, Greece, June 13–15, 2023, Proceedings / / edited by Georgios Goumas, Sven Tomforde, Jürgen Brehm, Stefan Wildermann, Thilo Pionteck |
Autore | Goumas Georgios |
Edizione | [1st ed. 2023.] |
Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
Descrizione fisica | 1 online resource (333 pages) |
Disciplina | 004.6 |
Altri autori (Persone) |
TomfordeSven
BrehmJürgen WildermannStefan PionteckThilo |
Collana | Lecture Notes in Computer Science |
Soggetto topico |
Computer networks
Computer systems Computer engineering Artificial intelligence Computers Computer Communication Networks Computer System Implementation Computer Engineering and Networks Artificial Intelligence Computer Hardware |
ISBN | 3-031-42785-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | 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. |
Record Nr. | UNINA-9910742494303321 |
Goumas Georgios | ||
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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