research-article

Block Walsh–Hadamard Transform-based Binary Layers in Deep Neural Networks

Authors:

Ahmet Enis CetinAuthors Info & Claims

ACM Transactions on Embedded Computing Systems, Volume 21, Issue 6

Article No.: 69, Pages 1 - 25

https://doi.org/10.1145/3510026

Published: 18 October 2022 Publication History

Abstract

Convolution has been the core operation of modern deep neural networks. It is well known that convolutions can be implemented in the Fourier Transform domain. In this article, we propose to use binary block Walsh–Hadamard transform (WHT) instead of the Fourier transform. We use WHT-based binary layers to replace some of the regular convolution layers in deep neural networks. We utilize both one-dimensional (1D) and 2D binary WHTs in this article. In both 1D and 2D layers, we compute the binary WHT of the input feature map and denoise the WHT domain coefficients using a nonlinearity that is obtained by combining soft-thresholding with the tanh function. After denoising, we compute the inverse WHT. We use 1D-WHT to replace the 1 × 1 convolutional layers, and 2D-WHT layers can replace the 3 × 3 convolution layers and Squeeze-and-Excite layers. 2D-WHT layers with trainable weights can be also inserted before the Global Average Pooling layers to assist the dense layers. In this way, we can reduce the number of trainable parameters significantly with a slight decrease in trainable parameters. In this article, we implement the WHT layers into MobileNet-V2, MobileNet-V3-Large, and ResNet to reduce the number of parameters significantly with negligible accuracy loss. Moreover, according to our speed test, the 2D-FWHT layer runs about 24 times as fast as the regular 3 × 3 convolution with 19.51% less RAM usage in an NVIDIA Jetson Nano experiment.

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Cited By

Luo YKhan THamdan EZhu XPan HOzevin DCetin A(2024)AlN Sputtering Parameter Estimation Using A Multichannel Parallel DCT Neural Network2024 IEEE 42nd VLSI Test Symposium (VTS)10.1109/VTS60656.2024.10538771(1-5)Online publication date: 22-Apr-2024
https://doi.org/10.1109/VTS60656.2024.10538771
Darabi NHashem MPan HCetin AGomes WTrivedi A(2024)ADC/DAC-Free Analog Acceleration of Deep Neural Networks With Frequency TransformationIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.337511132:6(991-1003)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1109/TVLSI.2024.3375111
Pan HZhu XAtici SCetin AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)A hybrid quantum-classical approach based on the hadamard transform for the convolutional layerProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619528(26891-26903)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619528
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Index Terms

Block Walsh–Hadamard Transform-based Binary Layers in Deep Neural Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 21, Issue 6

November 2022

498 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3561948

Editor:
Tulika Mitra
National University of Singapore, Singapore

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 18 October 2022

Online AM: 26 January 2022

Accepted: 30 December 2021

Revised: 25 November 2021

Received: 15 July 2021

Published in�TECS�Volume 21, Issue 6

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Cited By

Luo YKhan THamdan EZhu XPan HOzevin DCetin A(2024)AlN Sputtering Parameter Estimation Using A Multichannel Parallel DCT Neural Network2024 IEEE 42nd VLSI Test Symposium (VTS)10.1109/VTS60656.2024.10538771(1-5)Online publication date: 22-Apr-2024
https://doi.org/10.1109/VTS60656.2024.10538771
Darabi NHashem MPan HCetin AGomes WTrivedi A(2024)ADC/DAC-Free Analog Acceleration of Deep Neural Networks With Frequency TransformationIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.337511132:6(991-1003)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1109/TVLSI.2024.3375111
Pan HZhu XAtici SCetin AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)A hybrid quantum-classical approach based on the hadamard transform for the convolutional layerProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619528(26891-26903)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619528
Cetin APan H(2023)Hybrid Binary Neural Networks: A Tutorial Review2023 IEEE 41st VLSI Test Symposium (VTS)10.1109/VTS56346.2023.10139963(1-12)Online publication date: 24-Apr-2023
https://doi.org/10.1109/VTS56346.2023.10139963
Pan HZhu XYe ZChen PCetin A(2023)Real-Time Wireless ECG-Derived Respiration Rate Estimation using an Autoencoder with a DCT LayerICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10094831(1-5)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10094831
Song TFan SJin JJin GFan L(2023)Exploring an efficient frequency-guidance transformer for single image derainingSignal, Image and Video Processing10.1007/s11760-023-02918-z18:3(2429-2438)Online publication date: 16-Dec-2023
https://doi.org/10.1007/s11760-023-02918-z
Xiang JZang YJiang HWang LLiu Y(2023)Soft threshold iteration-based anti-noise compressed sensing image reconstruction networkSignal, Image and Video Processing10.1007/s11760-023-02686-w17:8(4523-4531)Online publication date: 25-Aug-2023
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Bin HJinhang LLili ZShi C(2023)Split frequency attention network for single image derainingSignal, Image and Video Processing10.1007/s11760-023-02601-317:7(3741-3748)Online publication date: 12-May-2023
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Karaoglu HEksioglu E(2023)DCTNet: deep shrinkage denoising via DCT filterbanksSignal, Image and Video Processing10.1007/s11760-023-02593-017:7(3665-3676)Online publication date: 2-Jun-2023
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Pan HBadawi DChen CWatts AKoyuncu ECetin A(2022)Deep Neural Network with Walsh-Hadamard Transform Layer For Ember Detection during a Wildfire2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00040(256-265)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00040

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