LAUSR

Recent advances of deep learning have produced encouraging results comparable to and in some cases superior to human experts. However, the large amount of data input has been a daunting task for deep learning to be widely applied in Internet of Things (IoT) with real-time processing. The goal of this paper is to develop smart and fast data processing scheme for more computational efficient deep learning to support adaptive and real-time applications, which will be able to increase the spectrum and energy efficiency in IoT. We propose to apply singular-value decomposition (SVD)-QR algorithm to preprocessing of deep learning for large scale data input. For the mass data input, we apply limited memory subspace optimization for SVD (LMSVD)-QR algorithm to increase the data processing speed. Simulation results in automated handwritten digit recognition show that SVD-QR and LMSVD-QR can tremendously reduce the number of input to deep learning neural network without losing its performance, and both can tremendously increase the data processing speed for deep learning in IoT.