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Related Experiment Video

Updated: Oct 19, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

570

Kronecker CP Decomposition With Fast Multiplication for Compressing RNNs.

Dingheng Wang, Bijiao Wu, Guangshe Zhao

    IEEE Transactions on Neural Networks and Learning Systems
    |September 17, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Recurrent neural networks (RNNs) can be compressed using Kronecker CANDECOMP/PARAFAC (KCP) decomposition. KCP-RNNs achieve high compression ratios and offer superior space and computation efficiency compared to other tensor decomposition methods.

    Related Experiment Videos

    Last Updated: Oct 19, 2025

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    570

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Recurrent neural networks (RNNs) excel at sequential data tasks but suffer from high computational and space complexity.
    • Existing tensor decomposition methods (TT, BT, TR, HT) offer high compression but lack both space and computational efficiency.

    Purpose of the Study:

    • To compress RNNs using a novel Kronecker CANDECOMP/PARAFAC (KCP) decomposition.
    • To develop fast algorithms for efficient multiplication with KCP-decomposed weights.
    • To evaluate the performance and efficiency of KCP-based RNN compression.

    Main Methods:

    • Proposed a novel Kronecker CANDECOMP/PARAFAC (KCP) decomposition for RNN compression.
    • Developed two fast algorithms for input-tensor multiplication with KCP-decomposed weights.
    • Experimented on multiple datasets (UCF11, Youtube Celebrities Face, UCF50, TIMIT, TED-LIUM, Spiking Heidelberg digits).

    Main Results:

    • KCP-RNNs demonstrate comparable accuracy to other tensor-decomposed RNNs.
    • Achieved a significant compression ratio of 278,219× with low-rank KCP.
    • KCP-RNNs exhibit superior space and computation efficiency over other tensor-decomposed RNNs.
    • Identified KCP's strong potential for parallel computing to accelerate neural network calculations.

    Conclusions:

    • KCP decomposition offers an efficient method for compressing RNNs.
    • KCP-RNNs provide a compelling balance of high compression, accuracy, and computational efficiency.
    • KCP shows promise for accelerating deep learning computations through parallel processing.