Abstract: An electronic device for determining global attention in a deep learning model is provided. The electronic device includes a hardware accelerator, a low-complex global attention generator, a parallel switch, and a series switch. The hardware accelerator is configured to process each tile of a full-frame image and the low complex global attention generator is configured to generate a channel attention map of the full-frame image. The parallel switch is configured to bypass a connection of the channel attention map with the hardware accelerator and a series switch, configured to gate the connection of the channel attention map with the hardware accelerator.

Abstract: The disclosure relates to method and system for on-device inference in a deep neural network (DNN). The method comprises: determining whether one or more layers of the DNN satisfy one of a first, a second and a third condition, the one or more layers including one or more convolution layers and one or more resampling layers; performing the on-device inference based on the determination, wherein performing the on-device inference comprises at least one of: optimizing the one or more convolution layers in the one or more parallel branches based on the one or more layers of the DNN satisfying the first condition, optimizing the at least one of the resampling layers based on the one or more layers of the DNN satisfying the second condition, and modifying operation of the at least one of the resampling layers based on the one or more layers of the DNN satisfying the third condition.