VLSI Implementation of the Low Power Neuromorphic Spiking Neural Network with Machine Learning Approach

Abstract

AbstractFor biomedical application, analysis of data through modern computational methodologies is required. Machine learning-based architectures enhance the way diagnosis is performed. The objective of this research work is to design a neuromorphic system using nanoelectronics and artificial intelligence for feature extraction and classification of medical data. The research area is a combination of nanoelectronics, computer technology, and biology. This research paper presents an extensive literature survey and descries the analysis, design, and implementation of neural networks based on human brain functionalities. The spiking neural networks (SNNs) architecture implementation in very large-scale integration will reduce the power consumption and miniaturize the device. A detailed review on various spiking neural networks architectures and methods is presented. In this research paper, an effort is made for the VLSI implementation of the spiking neural architecture. The implementation is carried out using Quartus tool and Spartan/Cyclone/Vertex Kits in 90 nm and 65 nm technology. Power, delay, and area are taken as the performance metrics.KeywordsFPGASpiking neural networkArtificial intelligenceNeuromorphic systemLow powerCMOSBiomedical