Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method

• URL: http://arxiv.org/abs/2209.09106v1
• Date: Tue, 6 Sep 2022 21:36:57 GMT
• Title: Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method
• Authors: Varun Mannam
• Abstract summary: Convolutional neural networks (CNNs) are a potential approach for object recognition and detection. A new approach based on the Hadamard transformation as an alternative to the convolution operation is demonstrated. The method is helpful for other computer vision tasks when the kernel size is smaller than the input image size.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The growing demand for the internet of things (IoT) makes it necessary to implement computer vision tasks such as object recognition in low-power devices. Convolutional neural networks (CNNs) are a potential approach for object recognition and detection. However, the convolutional layer in CNN consumes significant energy compared to the fully connected layers. To mitigate this problem, a new approach based on the Hadamard transformation as an alternative to the convolution operation is demonstrated using two fundamental datasets, MNIST and CIFAR10. The mathematical expression of the Hadamard method shows the clear potential to save energy consumption compared to convolutional layers, which are helpful with BigData applications. In addition, to the test accuracy of the MNIST dataset, the Hadamard method performs similarly to the convolution method. In contrast, with the CIFAR10 dataset, test data accuracy is dropped (due to complex data and multiple channels) compared to the convolution method. Finally, the demonstrated method is helpful for other computer vision tasks when the kernel size is smaller than the input image size.

Related papers

• Heterogenous Memory Augmented Neural Networks [84.29338268789684]
  We introduce a novel heterogeneous memory augmentation approach for neural networks. By introducing learnable memory tokens with attention mechanism, we can effectively boost performance without huge computational overhead. We show our approach on various image and graph-based tasks under both in-distribution (ID) and out-of-distribution (OOD) conditions.
  arXiv  Detail & Related papers  (2023-10-17T01:05:28Z)
• FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution [4.577685231084759]
  This paper proposes a Feature-Enhanced Neural Implicit Representation (FFEINR) for super-resolution of flow field data. It can take full advantage of the implicit neural representation in terms of model structure and sampling resolution. The training process of FFEINR is facilitated by introducing feature enhancements for the input layer.
  arXiv  Detail & Related papers  (2023-08-24T02:28:18Z)
• Deep Multi-Threshold Spiking-UNet for Image Processing [51.88730892920031]
  This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart.
  arXiv  Detail & Related papers  (2023-07-20T16:00:19Z)
• Input Layer Binarization with Bit-Plane Encoding [4.872439392746007]
  We present a new method to binarize the first layer using directly the 8-bit representation of input data. The resulting model is fully binarized and our first layer binarization approach is model independent.
  arXiv  Detail & Related papers  (2023-05-04T14:49:07Z)
• Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study [0.0]
  This paper proposes a CNN algorithm for the Alamosa Canyon Bridge as a real structure. Three different CNN models were considered to predict one and two malfunctioned sensors. The accuracy of the model was increased by adding a convolutional layer.
  arXiv  Detail & Related papers  (2022-04-11T23:24:03Z)
• Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision [64.71260357476602]
  Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames. Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks. We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
  arXiv  Detail & Related papers  (2021-12-06T23:45:58Z)
• Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution [80.93870349019332]
  We propose a novel HSISR method that uses Transformer instead of CNN to learn the prior of HSIs. Specifically, we first use the gradient algorithm to solve the HSISR model, and then use an unfolding network to simulate the iterative solution processes.
  arXiv  Detail & Related papers  (2021-11-27T15:38:57Z)
• Multi-objective Evolutionary Approach for Efficient Kernel Size and Shape for CNN [12.697368516837718]
  State-of-the-art development in CNN topology, such as VGGNet and ResNet, have become increasingly accurate. These networks are computationally expensive involving billions of arithmetic operations and parameters. This paper considers optimising the computational resource consumption by reducing the size and number of kernels in convolutional layers.
  arXiv  Detail & Related papers  (2021-06-28T14:47:29Z)
• Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration [83.84684675841167]
  We propose a novel encoding scheme using -1, +1 to decompose quantized neural networks (QNNs) into multi-branch binary networks. We validate the effectiveness of our method on large-scale image classification, object detection, and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2021-06-18T03:11:15Z)
• Random Features for the Neural Tangent Kernel [57.132634274795066]
  We propose an efficient feature map construction of the Neural Tangent Kernel (NTK) of fully-connected ReLU network. We show that dimension of the resulting features is much smaller than other baseline feature map constructions to achieve comparable error bounds both in theory and practice.
  arXiv  Detail & Related papers  (2021-04-03T09:08:12Z)
• Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications [0.0]
  This paper seeks to present and discuss one such method, namely Convolutional Neural Networks (CNNs) CNNs are deep neural networks that use a special linear operation called convolution. This paper discusses two applications of convolution that have proven to be very effective in practice.
  arXiv  Detail & Related papers  (2020-11-25T19:03:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.