A Novel Execution Time Prediction Scheme for Efficient Physical AI Resource Management

Physical AI enables reliable and timely operations of autonomous systems such as robots and smart manufacturing equipment under diverse and dynamic execution environments. In these environments, computing resources are often limited, shared among tasks, and fluctuate over time. This makes it difficult to guarantee that tasks meet timing constraints. As a result, resource-aware execution time prediction becomes essential for efficient resource management in physical AI systems. However, existing methods typically assume specific environments or static resource usage and often fail to generalize to new environments. In this paper, we propose CARE-D (Calibration-Assisted Resource-aware Execution time prediction), which trains a deep neural network to model the nonlinear relationships among hardware characteristics, resource levels, and task features across environments. The model predicts the execution time of tasks under diverse hardware and dynamically allocated computing resources, using a few execution records from new environments. CARE-D applies few-history-based calibration using only 1 to k execution records from target environments to adjust predictions without retraining the model. Experiments show that CARE-D improves prediction accuracy by about 7.3% over zero-history predictors within a 10% relative error and outperforms regression and deep learning baselines, using only one to five records per target environment.