Abstract: According to an embodiment of the disclosure, a method may include providing, by the electronic device, a plurality of Pareto fronts based on at least two performance parameters. The method may include identifying, by the electronic device, an optimal Pareto front from among the plurality of Pareto fronts. The method may include providing, by the electronic device, a second AI model iteratively. The method may include identifying, by the electronic device, whether the second AI model belongs to the optimal Pareto front. The method may include identifying, by the electronic device, the at least two performance parameters corresponding to the second AI model based on identifying that the second AI model belongs to the optimal Pareto front. The method may include obtaining, by the electronic device, the second AI model based on identifying that the second AI model meets one or more predetermined performance parameters.

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: Disclosed herein is a method and an optimization unit for optimizing and/or improving efficiency of resource utilization in an embedded computing system executing Artificial Intelligence (AI) applications. The method includes: detecting, by an optimization unit comprising processing circuitry and/or executable program instructions configured in the embedded computing system, a launch of an AI application on the embedded computing system; retrieving a runtime profile corresponding to the AI application, the runtime profile indicating resource requirements for executing the AI application; and configuring a runtime environment of the embedded computing system for the AI application based on the runtime profile corresponding to the AI application.