Energy-Efficient Algorithms for Machine Learning on Embedded Systems

Abstract

This article delves into the domain of energy-efficient algorithms specifically designed for machine learning tasks executed on embedded systems. As the need for intelligent applications expands across various sectors, integrating machine learning capabilities into devices with limited resources becomes increasingly vital. However, the energy limitations of such systems present notable obstacles. Consequently, researchers have been devising algorithms optimized to operate with minimal energy consumption. This abstract provides a concise overview of the main themes and findings explored within the paper, offering insights into the challenges, strategies, and practical implementations of energy-efficient machine learning algorithms on embedded systems.