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Architecture of computing systems = ...
ARCS (Conference) (2022 :)

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  • Architecture of computing systems = 35th International Conference, ARCS 2022, Heilbronn, Germany, September 13-15, 2022 : proceedings /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Architecture of computing systems/ edited by Martin Schulz ... [et al.].
    其他題名: 35th International Conference, ARCS 2022, Heilbronn, Germany, September 13-15, 2022 : proceedings /
    其他題名: ARCS 2022
    其他作者: Schulz, Martin.
    團體作者: ARCS (Conference)
    出版者: Cham :Springer International Publishing : : 2022.,
    面頁冊數: xvii, 287 p. :ill., digital ;24 cm.
    內容註: 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.
    Contained By: Springer Nature eBook
    標題: Computer architecture - Congresses. -
    電子資源: https://doi.org/10.1007/978-3-031-21867-5
    ISBN: 9783031218675
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W9447777 電子資源 11.線上閱覽_V 電子書 EB QA76.9.A73 A73 2022 一般使用(Normal) 在架 0
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