
Parallel execution of deep learning inference across heterogeneous mobile processors holds potential to accelerate on-device intelligence, but its practical effectiveness is limited by unsupported operators, process fallbacks, and the need to balance resource utilization with overall system performance.


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evaluating-the-potential-and-limitations-of-parallel-deep-learning-inference-on-heterogeneous-mobile-processors


Evaluating the Potential and Limitations of Parallel Deep Learning Inference on Heterogeneous Mobile Processors
