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Support for offloading model computation to mobile or embedded processor cores.
Distinct from Hardware Acceleration: Distinct from Hardware Acceleration: focuses on specific mobile/embedded chipset optimization rather than general GPU acceleration.
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This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation
Executes models on mobile or embedded chipsets by offloading computation to dedicated processor cores.