Structured Bayesian Compression for Deep models in mobile enabled devices for connected healthcare

TL;DR

This paper proposes a structured Bayesian compression technique to reduce the size and computational demands of deep neural networks, enabling efficient deployment on mobile healthcare devices.

Contribution

It introduces a novel Bayesian compression method tailored for deep models, optimizing them for mobile and connected healthcare applications.

Findings

Significant reduction in model size and energy consumption

Maintained high accuracy in medical data analysis

Enhanced suitability for mobile healthcare devices

Abstract

Deep Models, typically Deep neural networks, have millions of parameters, analyze medical data accurately, yet in a time-consuming method. However, energy cost effectiveness and computational efficiency are important for prerequisites developing and deploying mobile-enabled devices, the mainstream trend in connected healthcare.