Latency-Aware Design of Real-Time Artificial Intelligence Systems

Real-time artificial intelligence systems face stringent latency constraints that fundamentally shape architectural decisions, particularly in robotics and autonomous systems where timing directly impacts safety and performance. This paper presents a systematic framework for latency-aware AI system design, treating response time as a first-class constraint alongside accuracy and throughput. We introduce a threelayer architectural model comprising deadline-aware scheduling, adaptive computation allocation, and graceful quality degradation mechanisms. Drawing from established patterns in robotic system architectures and closed-loop control, the framework incorporates predictive latency estimation enabling proactive resource management and dynamic model selection based on available time budgets. Evaluation across robotic perception, computer vision, and decision-making tasks demonstrates that latency-aware designs achieve 94% deadline compliance compared to 67% for conventional approaches, while maintaining 91% of baseline accuracy. The proposed design principles provide practitioners with actionable guidance for building AI systems that reliably meet real-time requirements in deployment contexts ranging from mobile manipulation robots to interactive applications