Abstract: A method for mixed precision quantization of an artificial intelligence (AI) model by an electronic device is included. The method includes performing, by the electronic device, perturbation in weights of each layer of a plurality of layers of the AI model for a pre-defined number of times, determining, by the electronic device, a change in an output of each layer of a plurality of layers of the AI model based on a perturbation in weights of each layer of the plurality of layers, determining, by the electronic device, a sensitivity metric for each layer of the plurality of layers of the AI model as a measure of the change in the output of each layer, assigning, by the electronic device, a bit-precision to each layer of the plurality of layers of the AI model based on the determined sensitivity metric, and performing, by the electronic device, the mixed precision quantization of the AI model using the bit-precision assigned to each layer of the plurality of layers of the AI model.

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: 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.