{"type":"ScholarlyArticle","title":"GraphPerf-RT: Graph-Driven Performance Modeling with Calibrated Uncertainty for OpenMP Scheduling on Heterogeneous Embedded SoCs","authors":[{"name":"Mohammad Pivezhandi","slug":"mohammad-pivezhandi","given":"Mohammad","family":"Pivezhandi","affiliation":""},{"name":"Mahdi Banisharif","slug":"mahdi-banisharif","given":"Mahdi","family":"Banisharif","affiliation":""},{"name":"Saeed Bakhshan","slug":"saeed-bakhshan","given":"Saeed","family":"Bakhshan","affiliation":""},{"name":"Abusayeed Saifullah","slug":"abusayeed-saifullah","given":"Abusayeed","family":"Saifullah","affiliation":""},{"name":"Ali Jannesari","slug":"ali-jannesari","given":"Ali","family":"Jannesari","affiliation":""}],"abstract":"Autonomous AI agents on embedded platforms require real-time, risk-aware scheduling under resource and thermal constraints. Classical heuristics struggle with workload irregularity, tabular regressors discard structural information, and model-free reinforcement learning (RL) risks overheating. We introduce GraphPerf-RT, an AI technology achieving deep learning accuracy at heuristic speeds (2-7ms). GraphPerf-RT is, to our knowledge, the first graph-grounded infrastructure unifying task DAG topology, CFG-derived code semantics, and runtime context (per-core DVFS, thermal state, utilization) in a heterogeneous graph with typed edges encoding precedence, placement, and contention. The architecture supports multi-task evidential heads with Normal-Inverse-Gamma uncertainty; we validate on makespan prediction for risk-aware scheduling. Experiments on three ARM platforms (Jetson TX2, Orin NX, RUBIK Pi) achieve R^2 = 0.81 on log-transformed makespan with Spearman rho = 0.95 and conservative uncertainty calibration (PICP = 99.9% at 95% confidence). Integration with four RL methods demonstrates that multi-agent model-based RL with GraphPerf-RT as the world model achieves 66% makespan reduction and 82% energy reduction versus model-free baselines, with zero thermal violations.","doi":"10.24963/ijcai.2026/722","url":"https://ijcai.org/proceedings/2026/722","pdf":"https://ijcai.org/proceedings/2026/0722.pdf","markdown":"https://ijcai.org/proceedings/2026/722.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/722","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":722,"pages":{"first":6491,"last":6499},"keywords":["Advanced AI4Tech: AI4Tech foundations","Advanced AI4Tech: Deep AI4Tech","Advanced AI4Tech: Data-driven AI4Tech"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}