Edge intelligence is a blooming inter-discipline concept and is vital in several smart world applications. Edge intelligence is the integration of artificial intelligence and edge computing. Edge computing is an advance of cloud computing that brings the computation, data storage, data transfer, and power closer to the occurrence of an event rather than from a central data server. The benefits of edge computing are speed, security, scalability, versatility, and reliability. Edge intelligence utilizes edge computing to access and analyze the data from locally harvested area and use artificial intelligence (AI) that enable the machine to make accurate decision and predictions of such data.
Edge intelligence owns a wide range of applications such as real-time and predictive healthcare, fraud detection and prevention in finance, autonomous car systems, intelligent device tracking, and real-time routing decisions for cab companies. Some of the challenges in edge intelligence are latency, network bandwidth, distributed computing, security, and accessibility. Recurrent neural networks (RNN) are used in edge computing and device applications. Recurrent neural networks (RNN) are the unique architecture of neural networks that helps in modeling sequential data and possess a wide range of applications such as speech recognition, predictive healthcare, language modeling and generating text, video tagging, weather forecasting, and so on. RNN achieves high accuracy at the cost of large memory and expensive computations. Edge intelligent RNNs provide better performance in training the neural network architecture for edge intelligence applications.