论文标题
使用HADAMARD方法的低能卷积神经网络（CNN）
Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method
论文作者
论文摘要
对物联网（IoT）的需求不断增长，因此有必要在低功率设备中实施计算机视觉任务，例如对象识别。卷积神经网络（CNN）是对象识别和检测的潜在方法。但是，与完全连接的层相比，CNN中的卷积层消耗了明显的能量。为了减轻这个问题，使用两个基本数据集（MNAIST和CIFAR10）证明了一种基于Hadamard转换作为卷积操作的替代方案的新方法。与卷积层相比，Hadamard方法的数学表达显示了节省能源消耗的明显潜力，而卷积层有助于BigData应用。此外，对于MNIST数据集的测试准确性，Hadamard方法的性能与卷积方法相似。相反，与卷积方法相比，使用CIFAR10数据集，测试数据精度（由于复杂的数据和多个通道）删除。最后，当内核大小小于输入图像大小时，所示方法对其他计算机视觉任务有帮助。
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.
