Architecture of Computing Systems: Lecture Notes in Computer Science
Editat de Martin Schulz, Carsten Trinitis, Nikela Papadopoulou, Thilo Piontecken Limba Engleză Paperback – 14 dec 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.
Din seria Lecture Notes in Computer Science
- 20%
Preț: 390.18 lei - 20%
Preț: 461.65 lei - 20%
Preț: 461.32 lei - 20%
Preț: 461.77 lei - 20%
Preț: 527.36 lei - 20%
Preț: 497.04 lei - 20%
Preț: 461.52 lei - 20%
Preț: 390.35 lei - 20%
Preț: 496.64 lei - 20%
Preț: 461.52 lei - 20%
Preț: 391.14 lei - 15%
Preț: 461.85 lei - 20%
Preț: 389.72 lei - 20%
Preț: 461.06 lei - 20%
Preț: 390.69 lei - 20%
Preț: 252.15 lei - 20%
Preț: 391.14 lei - 20%
Preț: 532.54 lei - 20%
Preț: 461.83 lei - 20%
Preț: 255.91 lei - 20%
Preț: 498.46 lei - 20%
Preț: 497.55 lei - 20%
Preț: 499.36 lei - 20%
Preț: 390.12 lei - 20%
Preț: 391.20 lei - 20%
Preț: 390.35 lei - 20%
Preț: 391.20 lei - 20%
Preț: 392.64 lei - 20%
Preț: 389.90 lei - 20%
Preț: 639.72 lei - 20%
Preț: 461.65 lei - 20%
Preț: 462.05 lei - 15%
Preț: 388.50 lei - 5%
Preț: 516.27 lei - 20%
Preț: 391.86 lei - 20%
Preț: 389.85 lei - 20%
Preț: 498.32 lei - 20%
Preț: 462.67 lei - 20%
Preț: 460.98 lei - 20%
Preț: 424.26 lei - 20%
Preț: 535.77 lei - 15%
Preț: 535.92 lei - 20%
Preț: 532.28 lei - 20%
Preț: 400.17 lei - 20%
Preț: 403.00 lei - 20%
Preț: 461.57 lei - 20%
Preț: 498.50 lei - 20%
Preț: 461.83 lei - 20%
Preț: 249.95 lei
Preț: 390.94 lei
Preț vechi: 488.68 lei
-20% Nou
Puncte Express: 586
Preț estimativ în valută:
69.17€ • 80.58$ • 60.40£
69.17€ • 80.58$ • 60.40£
Carte disponibilă
Livrare economică 27 decembrie 25 - 10 ianuarie 26
Livrare express 16-20 decembrie pentru 33.45 lei
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9783031218668
ISBN-10: 3031218663
Pagini: 308
Ilustrații: XVII, 287 p. 137 illus., 98 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.47 kg
Ediția:1st edition 2022
Editura: Springer
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
ISBN-10: 3031218663
Pagini: 308
Ilustrații: XVII, 287 p. 137 illus., 98 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.47 kg
Ediția:1st edition 2022
Editura: Springer
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
Cuprins
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.