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Applied Reconfigurable Computing. Architectures, Tools, and Applications

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Cover of 'Applied Reconfigurable Computing. Architectures, Tools, and Applications'

Table of Contents

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    Book Overview
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    Chapter 1 100% Visibility at MHz Speed: Efficient Soft Scan-Chain Insertion on AMD/Xilinx FPGAs
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    Chapter 2 FPGA-Accelerated Tersoff Multi-body Potential for Molecular Dynamics Simulations
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    Chapter 3 A Runtime Programmable Accelerator for Convolutional and Multilayer Perceptron Neural Networks on FPGA
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    Chapter 4 A Multi-FPGA Scalable Framework for Deep Reinforcement Learning Through Neuroevolution
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    Chapter 5 Development Progress of SWLBM a Framework Based on Lattice Boltzmann Method for Fluid Dynamics Simulation
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    Chapter 6 Entropy-Based Early-Exit in a FPGA-Based Low-Precision Neural Network
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    Chapter 7 FPGA-Extended General Purpose Computer Architecture
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    Chapter 8 Multi-spectral In-Vivo FPGA-Based Surgical Imaging
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    Chapter 9 Hardware-Aware Optimizations for Deep Learning Inference on Edge Devices
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    Chapter 10 IPEC: Open-Source Design Automation for Inter-Processing Element Communication
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    Chapter 11 Light-Weight Permutation Generator for Efficient Convolutional Neural Network Data Augmentation
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    Chapter 12 Real-Time Embedded Object Tracking with Discriminative Correlation Filters Using Convolutional Features
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    Chapter 13 VenOS: A Virtualization Framework for Multiple Tenant Accommodation on Reconfigurable Platforms
Attention for Chapter 9: Hardware-Aware Optimizations for Deep Learning Inference on Edge Devices
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Chapter title
Hardware-Aware Optimizations for Deep Learning Inference on Edge Devices
Chapter number 9
Book title
Applied Reconfigurable Computing. Architectures, Tools, and Applications
Published by
Springer, Cham, October 2022
DOI 10.1007/978-3-031-19983-7_9
Book ISBNs
978-3-03-119982-0, 978-3-03-119983-7
Authors

Markus Rognlien, Zhiqiang Que, Jose G. F. Coutinho, Wayne Luk, Rognlien, Markus, Que, Zhiqiang, Coutinho, Jose G. F., Luk, Wayne

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