LAUSR

Although deep neural network (DNN) models have been extensively studied, they are often too complex to directly execute in real time on smart cameras with limited resources. Time-domain (TD) DNN models that incorporate frequency-domain (FD) layers, also known as hybrid-domain models, have been developed to mitigate the above problem; however, these require time/FD transforms. Currently, a pure FD DNN does not exist. Thus, our study proposes the first of such, along with our lightweight time/FD transform. Our model ensures that the networks perform faster on smart cameras and are more memory and energy efficient, both of which are important for smart cameras utilizing edge computing. Unlike existing TD or hybrid-domain studies, our model optimizes several internal neural network layers and implements a lightweight time/FD transform to reduce the number of calculations. More importantly, our study is the first to realize an FD fully connected layer, which can better represent a spectral feature distribution. The experimental results show that our accuracy slightly outperforms that of the existing time and hybrid-domain studies. In addition, our model’s inference speed on the edge computing platform was shown to be faster by a maximum of 52.01% for the MNIST data set and 52.00% for the CIFAR-10 data set. Furthermore, our model can improve frames per second (FPS) by at least 52.00% and memory usage by 43.64%, and save approximately 26.09% of power consumption for the MNIST data set.