Abstract: A method for generating a dynamic neural network includes: utilizing a neural architecture search (NAS) method to obtain a searched result, wherein the searched result comprises a plurality of sub-networks; combining the plurality of sub-networks to generate a combined neural network; and fine-tuning the combined neural network to generate the dynamic neural network.

Abstract: A neural network optimization method with a preview mechanism includes: in a preview stage, building an optimization space and obtaining multiple previewed results from the optimization space; generating an updating signal according to a reference value corresponding to the multiple previewed results, and processing the optimization space received in the preview stage according to the reference value; and in a view stage, receiving the optimization space and the updating signal, and processing the optimization space received in the view stage according to the updating signal to generate an optimization result.

Abstract: A system for dynamically adjusting neural network efficiency of a dynamic neural network running on a device includes a detector and a signal generator. The detector is arranged to detect a change of a status of the device, to generate a trigger signal. The signal generator is arranged to generate a control signal according to the trigger signal, to dynamically adjust the neural network efficiency of the dynamic neural network.

Abstract: A method of performing automatic tuning on a deep learning model includes: utilizing an instruction-based learned cost model to estimate a first type of operational performance metrics based on a tuned configuration of layer fusion and tensor tiling; utilizing statistical data gathered during a compilation process of the deep learning model to determine a second type of operational performance metrics based on the tuned configuration of layer fusion and tensor tiling; performing an auto-tuning process to obtain a plurality of optimal configurations based on the first type of operational performance metrics and the second type of operational performance metrics; and configure the deep learning model according to one of the plurality of optimal configurations.

Abstract: A winding structure of motor stator includes a stator core, two insulation members, an upper cap, a lower cap, and a coil. The two insulation members are disposed on two winding grooves of the stator core respectively. The upper cap and the lower cap are disposed on a top end and a bottom end of the stator core respectively. The coil is wound on the upper cap, the two insulation members, and the lower cap. Stopping pieces facing each other are formed on two sides of each of the upper cap and the lower cap. Each of the stopping pieces is separated from the coil without contact.

Abstract: A winding structure of motor stator includes a stator core, two insulation members, an upper cap, a lower cap, and a coil. The two insulation members are disposed on two winding grooves of the stator core respectively. The upper cap and the lower cap are disposed on a top end and a bottom end of the stator core respectively. The coil is wound on the upper cap, the two insulation members, and the lower cap. Stopping pieces facing each other are formed on two sides of each of the upper cap and the lower cap. Each of the stopping pieces is separated from the coil without contact.

Abstract: An example method includes at an electronic device with one or more processors and memory: obtaining first data representing a user-generated keyboard path for one or more words; obtaining second data representing a synthetic keyboard path for the one or more words; generating, using a first instance of a generative network, based on the first data and the second data, third data representing a modification of the synthetic keyboard path; determining whether the third data represent a second user-generated keyboard path; determining whether the third data represent the one or more words; and in accordance with a determination that the third data represent a second user-generated keyboard path and a determination that the third data represent the one or more words: training a model for keyboard path recognition based on the third data.