Learning Performance Optimization for Edge AI System with Time and Energy Constraints

TL;DR

This paper develops a comprehensive model and optimization framework to enhance learning performance in Edge AI systems by balancing time and energy constraints, validated through simulations.

Contribution

It introduces a rigorous convergence analysis and formulates a joint optimization approach for data collection and training in Edge AI with energy and time limits.

Findings

Convergence analysis accurately predicts learning performance impacts.

Proposed algorithms effectively optimize data collection and training rounds.

Simulation results confirm improved energy efficiency and learning outcomes.

Abstract

Edge AI, which brings artificial intelligence to the edge of the network for real-time processing and decision-making, has emerged as a transformative technology across various applications. However, the deployment of Edge AI systems faces significant challenges due to high energy consumption and extended operation time. In this paper, we consider an Edge AI system which integrates the data acquisition, computation and communication processes, and focus on improving learning performance of this system. We model the time and energy consumption of different processes and perform a rigorous convergence analysis to quantify the impact of key system parameters, such as the amount of collected data and the number of training rounds, on the learning performance. Based on this analysis, we formulate a system-wide optimization problem that seeks to maximize learning performance under given time and energy constraints. We explore both homogeneous and heterogeneous device scenarios, developing low-complexity algorithms based on one-dimensional search and alternating optimization to jointly optimize data collection time and training rounds. Simulation results validate the accuracy of our convergence analysis and demonstrate the effectiveness of the proposed algorithms, providing valuable insights into designing energy-efficient Edge AI systems under real-world conditions.