Abstract: A method for selecting an artificial intelligence (AI) model in neural architecture search, includes: measuring a scale of receptive field for a plurality of neural network layers corresponding to each of a plurality of candidate AI models; determining a first score for a first group of neural network layers among the plurality of neural network layers based on the scale of the receptive field for the first group of neural network layers, the scale of the receptive field for each of the first group of neural network layers being smaller than a size of an object; determining a second score for a second group of neural network layers among the plurality of neural network layers based on the scale of the receptive field for the second group of neural network layers, the scale of the receptive field for each of the second group of neural network layers being greater than the size of the object; determining a third score for each of the plurality of candidate AI models as a function of the first score and the seco

Abstract: Provided are systems and methods for optimizing neural networks for on-device deployment in an electronic device. A method for optimizing neural networks for on-device deployment in an electronic device includes receiving a plurality of neural network (NN) models, fusing at least two NN models from the plurality of NN models based on at least one layer of each of the at least two NN models, to generate a fused NN model, identifying at least one redundant layer from the fused NN model, and removing the at least one redundant layer to generate an optimized NN model.

Abstract: Various embodiments of the disclosure disclose a method for quantizing a Deep Neural Network (DNN) model in an electronic device. The method includes: estimating, by the electronic device, an activation range of each layer of the DNN model using self-generated data (e.g. retro image, audio, video, etc.) and/or a sensitive index of each layer of the DNN model; quantizing, by the electronic device, the DNN model based on the activation range and/or the sensitive index; and allocating, by the electronic device, a dynamic bit precision for each channel of each layer of the DNN model to quantize the DNN model.

Abstract: A method, an apparatus, and a system for configuring a neural network across heterogeneous processors are provided. The method includes creating a unified neural network profile for the plurality of processors; receiving at least one request to perform at least one task using the neural network; determining a type of the requested at least one task as one of an asynchronous task and a synchronous task; and parallelizing processing of the neural network across the plurality of processors to perform the requested at least one task, based on the type of the requested at least one task and the created unified neural network profile.

Abstract: A method and a system for transmitting data from a first electronic device to a second electronic device using a human body as a signal transmission path, are provided. The method includes detecting a first touch event on the first electronic device, the first touch event corresponding to the data. The method further includes receiving indication of a second touch event that is detected on the second electronic device, the second touch event corresponding to a memory location in the second electronic device. The method further includes capacitively transmitting the data from the first electronic device to the memory location through the human body in response to the detecting the first touch event and the receiving the indication of the second touch event.

Abstract: A method and a system for transmitting data from a first electronic device to a second electronic device using a human body as a signal transmission path, are provided. The method includes detecting a first touch event on the first electronic device, the first touch event corresponding to the data. The method further includes receiving indication of a second touch event that is detected on the second electronic device, the second touch event corresponding to a memory location in the second electronic device. The method further includes capacitively transmitting the data from the first electronic device to the memory location through the human body in response to the detecting the first touch event and the receiving the indication of the second touch event.