Efficient Transformations in Deep Learning Convolutional Neural Networks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915352318836736 |
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| author | Yilmaz, Berk Harvey, Daniel Fidel Dhuri, Prajit |
| author_facet | Yilmaz, Berk Harvey, Daniel Fidel Dhuri, Prajit |
| contents | This study investigates the integration of signal processing transformations -- Fast Fourier Transform (FFT), Walsh-Hadamard Transform (WHT), and Discrete Cosine Transform (DCT) -- within the ResNet50 convolutional neural network (CNN) model for image classification. The primary objective is to assess the trade-offs between computational efficiency, energy consumption, and classification accuracy during training and inference. Using the CIFAR-100 dataset (100 classes, 60,000 images), experiments demonstrated that incorporating WHT significantly reduced energy consumption while improving accuracy. Specifically, a baseline ResNet50 model achieved a testing accuracy of 66%, consuming an average of 25,606 kJ per model. In contrast, a modified ResNet50 incorporating WHT in the early convolutional layers achieved 74% accuracy, and an enhanced version with WHT applied to both early and late layers achieved 79% accuracy, with an average energy consumption of only 39 kJ per model. These results demonstrate the potential of WHT as a highly efficient and effective approach for energy-constrained CNN applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_16418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Transformations in Deep Learning Convolutional Neural Networks Yilmaz, Berk Harvey, Daniel Fidel Dhuri, Prajit Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing Signal Processing 68T07, 68T10, 94A08, 42C10 This study investigates the integration of signal processing transformations -- Fast Fourier Transform (FFT), Walsh-Hadamard Transform (WHT), and Discrete Cosine Transform (DCT) -- within the ResNet50 convolutional neural network (CNN) model for image classification. The primary objective is to assess the trade-offs between computational efficiency, energy consumption, and classification accuracy during training and inference. Using the CIFAR-100 dataset (100 classes, 60,000 images), experiments demonstrated that incorporating WHT significantly reduced energy consumption while improving accuracy. Specifically, a baseline ResNet50 model achieved a testing accuracy of 66%, consuming an average of 25,606 kJ per model. In contrast, a modified ResNet50 incorporating WHT in the early convolutional layers achieved 74% accuracy, and an enhanced version with WHT applied to both early and late layers achieved 79% accuracy, with an average energy consumption of only 39 kJ per model. These results demonstrate the potential of WHT as a highly efficient and effective approach for energy-constrained CNN applications. |
| title | Efficient Transformations in Deep Learning Convolutional Neural Networks |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing Signal Processing 68T07, 68T10, 94A08, 42C10 |
| url | https://arxiv.org/abs/2506.16418 |