LATENCY-AWARE PERFORMANCE ANALYSIS OF NEURAL NETWORKS FOR REAL-TIME INTELLIGENT SYSTEMS

Abstract

Real-time intelligent systems demand not only high accuracy but also strict latency constraints to ensure timely decision-making. Neural networks have demonstrated remarkable performance across various intelligent applications; however, their computational complexity often introduces significant latency. This paper presents a comprehensive latency-aware performance analysis of neural networks designed for realtime intelligent systems. The study focuses on evaluating the trade-offs between accuracy, computational cost, and response time. Various neural network architectures are analyzed under different latency constraints. Optimization strategies such as model compression, pruning, and efficient inference techniques are examined. Experimental results demonstrate that latency-aware design significantly improves real-time performance without major accuracy degradation. The findings highlight the importance of incorporating latency as a core evaluation metric. The proposed analysis framework supports the development of efficient real-time intelligent systems.