Such an exponential rise in the use of the world wide web due to social networks started to fetch huge volume of data across diverse domains in short period of time. It has paved way for the great proliferation of Internet into the life of even the ordinary people. Social networks have greatly influenced the usage pattern of Internet by common man.

KEYWORDS Artificial intelligence (AI), application specific integrated circuit (ASIC), artificial neural network (ANN), central processing unit (CPU), field programmable gate array (FPGA), graphics processing unit (GPU), machine learning (ML), neurochip This paper also presents the requirements, design issues and optimization techniques for building hardware architecture of neural networks. In this paper, we discuss the purpose, representation and classification methods for developing hardware for machine learning with the main focus on neural networks.

Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. The applications of machine learning range from speech and object recognition to analysis and prediction of finance markets. Machine learning plays a significant role in developing these models. All of these require models that can automatically analyse large complex data and deliver quick accurate results – even on a very large scale. Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions in real time without human intervention are playing critical role in this age.

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