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Applied Reconfigurable Computing: 15th International Symposium, ARC 2019, Darmstadt, Germany, April 911, 2019, Proceedings 2019 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 418 pages, height x width: 235x155 mm, weight: 658 g, 110 Illustrations, color; 107 Illustrations, black and white; XIII, 418 p. 217 illus., 110 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 11444
  • Izdošanas datums: 29-Mar-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030172260
  • ISBN-13: 9783030172268
  • Mīkstie vāki
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  • Formāts: Paperback / softback, 418 pages, height x width: 235x155 mm, weight: 658 g, 110 Illustrations, color; 107 Illustrations, black and white; XIII, 418 p. 217 illus., 110 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 11444
  • Izdošanas datums: 29-Mar-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030172260
  • ISBN-13: 9783030172268
This book constitutes the proceedings of the 15th International Symposium on Applied Reconfigurable Computing, ARC 2019, held in Darmstadt, Germany, in April 2019.





The 20 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 52 submissions. In addition, the volume contains 1 invited paper. The papers were organized in topical sections named: Applications; partial reconfiguration and security; image/video processing; high-level synthesis; CGRAs and vector processing; architectures; design frameworks and methodology; convolutional neural networks.

Applications.- Fault-Tolerant Architecture for On-Board Dual-Core Synthetic-Aperture Radar Imaging.- Optimizing CNN-based Hyperspectral Image Classification on FPGAs.- Supporting Columnar In-Memory Formats on FPGA: The Hardware Design of Fletcher for Apache Arrow.- A Novel Encoder for TDCs.- A Resource Reduced Application-Specific FPGA Switch.- Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications.- Partial Reconfiguration and Security.- Probabilistic Performance Modelling when using Partial Reconfiguration to Accelerate Streaming Applications with Non-Deterministic Task Scheduling.- Leveraging the Partial Reconfiguration Capability of FPGAs for Processor-Based Fail-Operational Systems.- (ReCo)Fuse Your PRC or Lose Security: Finally Reliable Reconfiguration-based Countermeasures on FPGAs.- Proof-Carrying Hardware versus the Stealthy Malicious LUT Hardware Trojan.- Secure Local Configuration of Intellectual Property Without a Trusted Third Party.- Image/Video Processing.- HiFlipVX: an Open Source High-Level Synthesis FPGA Library for Image Processing.- Real-time FPGA implementation of connected component labelling for a 4K video stream.- A Scalable FPGA-based Architecture for Depth Estimation in SLAM.- High-Level Synthesis.- Evaluating LULESH Kernels on OpenCL FPGA.- The TaPaSCo Open-Source Toolflow for the Automated Composition of Task-Based Parallel Reconfigurable Computing Systems.- Graph-based Code Restructuring Targeting HLS for FPGAs.- CGRAs and Vector Processing.- UltraSynth: Integration of a CGRA into a Control Engineering Environment.- Exploiting reconfigurable vector processing for energy-efficient computation in 3D-stacked memories.- Automatic Toolflow for VCGRA Generation to Enable CGRA Evaluation for Arithmetic Algorithms.- Architectures.- ReM: a Reconfigurable Multipotent Cell for New Distributed Reconfigurable Architectures.- Update or Invalidate: Influence of Coherence Protocols on Configurable HW Accelerators.- Design Frameworks and Methodology.- Hybrid Prototyping for Manycore Design and Validation.- Evaluation of FPGA Partitioning Schemes for Time and Space Sharing of Heterogeneous Tasks.- Invited Talk.- Third Party CAD Tools for FPGA Design | A Survey of the Current Landscape.- Convolutional Neural Networks.- Filter-wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation.- Exploring Data Size to Run Convolutional Neural Networks in Low Density FPGAs.- Faster Convolutional Neural Networks in Low Density FPGAs using Block Pruning.