Review Based on Different Deep Learning Architecture and their Application

Electronics and Communication Engineering

Deep neural networks (DNNs) are currently widely used for much artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI system. An up-to-date overview is provided on four deep learning architectures, namely, Artificial Neural Networks, Auto encoder, convolutional neural network, deep belief network, and restricted Boltzmann machine, Recurrent Neural Network. Different types of deep neural networks are surveyed and recent progress is summarized.

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