Programmable Big Data Processing Framework to Reduce On-Chip Communications and Computations Towards Reducing Energy of the Processing

Abstract

Multi-Sensor health and wellness tracking systems are utilized to forecast future occasions of our health and wellness system. Each sensor produces a whopping quantity of data per 2nd and also requires to be refined in real-time. At the very same time, health and wellness tracking systems are battery ran, hence they have stiff restraints on power and also the location of the processing platform. Furthermore, health and wellness surveillance systems need to be precise, hence we adjust artificial intelligence methods to boost discovery precision. We recommend a programmable Big Data Processing structure to decrease on-chip interactions as well as calculations, therefore minimizing the power of the processing. We incorporate a low-overhead laying out structure with a low-power programmable PENC many-core platform. The laying out method decreases the data interactions and also calculations, furthermore processing time is reduced by parallel processing on the many-core platform. For the presentation we reveal seizure discovery application with 22-channel of electroencephalograph (EEG), each network creates 256 examples per 2nd calling for overall of 88 Kbps data price. The calculations are lowered by 16 while the power intake of processing is minimized approximately 68%. For compression prices of 2-16, the seizure discovery efficiency for the level of sensitivity and also uniqueness is broken down by 2.07% and also 2.97%, specifically for Logistic Regression classifier.