The automated translation, speech recognition and synthesis, object detection as well as emotion recognition are well known complex tasks that modern smartphone can solve. It became possible with intensive usage of algorithms of Artificial Intelligence and Machine Learning. Most popular now are implementations of deep neural networks and deep learning algorithms. Such algorithms are widely used in all verticals and need hardware accelerators as well as deep cooperation between both software and hardware parts. The mentioned task became very actual during embedding of cloud-based algorithms into systems with limited computing capabilities, small physical size, and extremely low power consumption. The aim of this paper is to compare existing software and hardware solutions dedicated to the development of artificial neural networks and deep learning applications. The paper is focused on three topics related to deep learning software frameworks, specialized GPU-based hardware, and prospects of deep learning acceleration using FPGA. The most popular software frameworks, such as Caffe, Theano, Torch, MXNet, Tensorflow, Neon, CNTK have been compared and analyzed in the paper. Advantages of GPU solutions based on CUDA and cuDNN frameworks have been described. Prospects of FPGA as high-speed and power-efficient solutions for deep learning algorithm design, especially in terms of combination with OpenCL language have been discussed in the paper.
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