HarmonyOS PC's AI runtime is built on an execution graph architecture that orchestrates AI workloads across heterogeneous compute units—CPU, GPU, and NPU. Unlike traditional sequential pipelines, the execution graph represents AI model inference as a directed acyclic graph (DAG) of operations, allowing the runtime to dynamically reorder, fuse, or parallelize tasks based on real-time resource availability and power constraints. This design is particularly effective for on-device AI scenarios where latency and energy efficiency are critical. The article details how the graph is constructed from model definitions, how operators are mapped to hardware backends, and how the runtime handles dynamic shapes and conditional branches. For developers working on AI frameworks or operating system-level AI support, this offers a concrete example of a graph-based runtime in a commercial OS. The approach shares similarities with TensorFlow's graph execution but is tailored for the resource-constrained environment of a PC platform with diverse accelerators.
This article explains how HarmonyOS PC structures its AI runtime around an execution graph model, enabling efficient scheduling across CPU, GPU, and NPU. The execution graph approach allows dynamic optimization of AI workloads, reducing latency and improving power efficiency. For developers building AI applications on HarmonyOS or studying runtime design, this provides insight into a production-grade implementation.