A Novel NoC-Based Neural Networks Design for Implementation of A Biosensor Solution

Abstract

Graphene-based nanosensors are used in many applications to monitor and survey physical or chemical phenomena. The use of Graphene improves the mechanical, electrochemical, optical, and magnetic properties of nanosensors due to its properties such as physical properties. But there are some characteristics and measurements that must be taken to manufacture such sensors. In this work, a Neural Network (NN) has been proposed that can predict the values of UV (ultraviolet) measurements, which are wavelength and absorption, based on XRD (X Ray Diffraction) measurements, namely diameter (in millimeters) and angle (theta in degrees). The proposed NN was developed on Network-on-Chip (NoC) to take advantage of the high level of parallelism and computing power provided by NoC. In addition, we used an adaptive XY routing algorithm due to its simplicity and allows exploiting multiple paths to route packets to their destination which reduces the number of lost packets due to the high injection rate without introducing any latency. Moreover, we proposed a mapping algorithm to extract maximum performance from the adaptive XY algorithm. The obtained results show the efficiency of the proposed architecture, as it allows packets to take different paths. Thus, the traffic is distributed across the network and significantly reduces packet loss. Moreover, the equal length of these paths allows avoidance of latency. HIGHLIGHTS Network-on-chip (NoC) is a powerful tool for implementing artificial neural networks, thanks to its high level of parallelism and computing power The Adaptive XY algorithm eliminates the need for a routing table and allows traffic to be distributed over the NoC The proposed mapping algorithm allows for better traffic management within the NoC, thereby enhancing performance in terms of reducing lost packets GRAPHICAL ABSTRACT