  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.
