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Efficient Hardware Architectures for Deep Convolutional Neural Network

https://doi.org/10.1109/tcsi.2017.2767204

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What this work says

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inferred← citeshttps://doi.org/10.1142/s0218126622502899openalex-snapshot

RNA: A Flexible and Efficient Accelerator Based on Dynamically Reconfigurable Computing for Multiple Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1142/s0218126623500755openalex-snapshot

A Resource Efficient CNN Accelerator for Sensor Signal Processing Based on FPGA

inferred← citeshttps://doi.org/10.1145/3338852.3339840openalex-snapshot

A FPGA parameterizable multi-layer architecture for CNNs

inferred← citeshttps://doi.org/10.21203/rs.3.rs-3820413/v1openalex-snapshot

Optimizing FPGA-Based Target Detection: A Hybrid Winograd-GEMM Accelerator with Enhanced Resource Efficiency and Throughput

inferred← citeshttps://doi.org/10.22214/ijraset.2022.46913openalex-snapshot

Design of Area-Power Efficient Parallel Fir Filter with Mux Based Full Adder

inferred← citeshttps://doi.org/10.32604/cmc.2020.012380openalex-snapshot

A Dynamically Reconfigurable Accelerator Design Using a Sparse-Winograd Decomposition Algorithm for CNNs

inferred← citeshttps://doi.org/10.32604/cmc.2023.045948openalex-snapshot

FPGA Optimized Accelerator of DCNN with Fast Data Readout and Multiplier Sharing Strategy

inferred← citeshttps://doi.org/10.3389/fenvs.2024.1291327openalex-snapshot

Predicting groundwater level using traditional and deep machine learning algorithms

inferred← citeshttps://doi.org/10.3390/agriculture12111849openalex-snapshot

FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive

inferred← citeshttps://doi.org/10.3390/ai6010008openalex-snapshot

Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays

inferred← citeshttps://doi.org/10.3390/app8122670openalex-snapshot

Dark Spot Detection in SAR Images of Oil Spill Using Segnet

inferred← citeshttps://doi.org/10.3390/electronics10232889openalex-snapshot

A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

inferred← citeshttps://doi.org/10.3390/electronics12244943openalex-snapshot

WRA-MF: A Bit-Level Convolutional-Weight-Decomposition Approach to Improve Parallel Computing Efficiency for Winograd-Based CNN Acceleration

inferred← citeshttps://doi.org/10.3390/electronics8020143openalex-snapshot

An Accelerator Architecture of Changeable-Dimension Matrix Computing Method for SVM

inferred← citeshttps://doi.org/10.3390/electronics8111321openalex-snapshot

Fast Convolutional Neural Networks in Low Density FPGAs Using Zero-Skipping and Weight Pruning

inferred← citeshttps://doi.org/10.3390/electronics9091526openalex-snapshot

A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems

inferred← citeshttps://doi.org/10.3390/electronics9111823openalex-snapshot

mNet2FPGA: A Design Flow for Mapping a Fixed-Point CNN to Zynq SoC FPGA

inferred← citeshttps://doi.org/10.3390/s20195558openalex-snapshot

An Accelerator Design Using a MTCA Decomposition Algorithm for CNNs

inferred← citeshttps://doi.org/10.3390/s22155874openalex-snapshot

Heuristic Method for Minimizing Model Size of CNN by Combining Multiple Pruning Techniques

inferred← citeshttps://doi.org/10.3390/s23042045openalex-snapshot

A Low-Power Hardware Architecture for Real-Time CNN Computing

inferred← citeshttps://doi.org/10.34133/plantphenomics.0234openalex-snapshot

Deep Learning Methods Using Imagery from a Smartphone for Recognizing Sorghum Panicles and Counting Grains at a Plant Level

inferred← citeshttps://doi.org/10.36227/techrxiv.22728788openalex-snapshot

S2RNN: Self-Supervised Reconfigurable Neural Network Hardware Accelerator for Machine Learning Applications

inferred← citeshttps://doi.org/10.48084/etasr.6761openalex-snapshot

Hardware Acceleration for Object Detection using YOLOv5 Deep Learning Algorithm on Xilinx Zynq FPGA Platform

inferred← citeshttps://doi.org/10.7256/2454-0714.2024.1.69919openalex-snapshot

Research and development of algorithms for the formation of an effective ensemble of convolutional neural networks for image classification

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