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Compressing Neural Networks Towards Edge Artificial Intelligence by Mayur Dhanaraj and Panos P. Markopoulos In the modern technological era, artificial intelligence (AI) is ubiquitous. Commonly used devices such as mobile phones, smart TVs, smart watches, and security surveillance cameras, among many others leverage AI in order to offer smart capabilities, revolutionizing everyday life. Some exciting and important applications of AI include self-driven cars, voice recognition, face recognition, disease diagnosis, real-time health monitoring, and enemy aircraft detection in defense, just to name a few. AI is the intersection of compute (including powerful graphical processing units (GPUs) and large storage capabilities), data (availability of large corpus of data to learn from), and algorithms to process this data and derive useful underlying patterns that enable autonomous decision making. These three pillars of AI are shown in Fig. 1.
Fig. 1: The Trinity of Artificial Intelligence. Thus, the success of AI can be attributed to the availability of large data sets, powerful computational resources, and effective data processing algorithms that enable the system to derive meaningful latent information from data, which in turn is used to make automatic decisions. Just as humans learn their surroundings through many examples and continuous reinforcement, an AI system also learns by looking at many examples of a certain object of interest and updates itself to generalize well on examples that it has not seen before. An artificial neural network (ANN) is an example of an algorithm that learns an underlying pattern from given data and thus imparts intelligence to the AI system. Traditionally, ANNs were inspired by the neural networks in the human brain. The key idea was to mimic the human brain by recreating artificial neurons on hardware which are inter-connected just as neurons are in a human brain. Similarly, the goal was to strengthen certain connections in an artificial neural network such that it generalizes well on unforeseen data. ANNs employ many layers, each consisting of multiple neurons (nodes). These neurons collect all the connections from a previous layer and aggregate it by means of mathematical operations. This procedure is repeated for all neurons in a layer. It is empirically shown that the layers of artificial neurons are able to learn abstract representation of the data, thereby deriving useful data features, resulting in autonomous decision making. Moreover, if large datasets are available, neural networks with many layers –deep neural networks (DNNs) perform better than shallow ones. A pictorial representation of a deep neural network is presented in the Fig. 2 below.
Fig. 2: Pictorial Representation of a Deep Neural Network.
18 | The ROCHESTER ENGINEER JUNE 2022
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