An Effective and Efficient Computation Architecture for Edge Computing Devices on IoMT-Based Deep Belief Networks

Abstract

The Internet of Medical Things (IoMT) is one of the growing and emerging technologies in recent trends. Faster computation is the major requirement of any edge computing device. Edge computing systems require Effective Computation Blocks (ECB) to store and process signals between users and the cloud. The time taken for trans-receiving and processing of the signal should be minimal, which is mentioned as latency. The ECB assures high-end power transmissions, especially in autonomous vehicles, robotic surgery, diagnosis, and medicine distributions. The ECB architecture is based on highly effective computation. The computation is independent of internet connectivity and therefore the major suspect is uncertainty. This work focuses on the development of sustainable approximation adder for edge devices of IoMT. This architecture performance is measured at the deep learning architectures which are familiar at the edge devices of cloud computing. In cases of low internet, the computing devices are slower, which causes all devices and applications to go down the track. By implementing the proposed adder (PAXA) at the edge, computing gets around the dependencies by locating data that is closer to the possibility, which speeds up applications and improves their availability and also in the applications where it requires high speed and low-power availability.