Design of a Structured Hypercube Network Chip Topology Model for Energy Efficiency in Wireless Sensor Network Using Machine Learning

Abstract

Network on chips (NoCs) 3D design expansion is continuously changing to produce energy-efficient NoCs. In this production, the major requirement is to have continuous monitoring with great effort of engineering process and policies which tries to incorporate the machine learning techniques for producing EE-NoCs (energy-efficient NoCs). The learning is a method of neural network system. This thought process of machine learning art in producing EE-NoCs resulted in better production. The internal architecture framed is Power Gate Deployment, voltage instant changeovers, and scaling in the frequenting simultaneous reduction in power. Multiprocessor architecture and platform have been introduced to extend the applicability of Moore's law. The solution for the multiprocessor system architecture is application-specific NoC architecture which are emerging as a leading technology. NoC can be useful in addressing many requirements such as inter-process communication, bandwidth, deadlock avoidance and routing structure. With power now the first order design constraint early stage estimation of NoC power, performance, and area has become important. The topology, switching and routing techniques are necessary in the NoC architecture design. This paper focuses on the n-dimension hypercube network on chip topological structure. It is used for more efficient performance and reliability for data communication. Therefore, in this paper, we are going to go through a bunch of different concepts, such as routing deadlock, and the router pipeline. Keywords Network on chip (NoC) • Power gate • Machine learning • Frequency scale • System on chip (SOC)This article is part of the topical collection "Computational Statistics" guest edited by Anish Gupta, Mike Hinchey, Vincenzo Puri, Zeev Zalevsky and Wan Abdul Rahim.

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