BACKGROUND

Neural networks have become standard machine learning tools to solve a variety of problems, such as computer vision and automatic speech recognition processing. However, neural networks require an extensive amount of computations to be performed, which lays burden on the power consumption and computation resources of computing devices.

For some computing devices, such as Internet of Things (IoT) devices, wearable devices, and mobile devices, power and computation resources are limited. Therefore, executing neural networks on these devices in an efficient manner can be a challenge.

SUMMARY OF THE DISCLOSURE

Embodiments of the disclosure provide a computer-implemented method for executing an activation function of a neural network. The method can include: receiving a plurality of input vectors of input data; generating, among the plurality of input vectors, an estimation value associated with a subset of an input vector based on a weight vector of the activation function; determining whether the estimation value associated with the subset of the input vector satisfies a threshold condition; and determining an output of the activation function based on the estimation value.

Embodiments of the disclosure also provide a system for executing a neural network. The system can include a memory storing a set of instructions; and at least one processor configured to execute the set of instructions to cause the system to perform: receiving a plurality of input vectors of input data; generating, among the plurality of input vectors, an estimation value associated with a subset of an input vector based on a weight vector of the activation function; determining whether the estimation value associated with the subset of the input vector satisfies a threshold condition; and determining an output of the activation function based on the estimation value.

Embodiments of the disclosure further provide a non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computer system to cause the computer system to perform a method for executing an activation function of a neural network. The method can include: receiving a plurality of input vectors of input data; generating, among the plurality of input vectors, an estimation value associated with a subset of an input vector based on a weight vector of the activation function; determining whether the estimation value associated with the subset of the input vector satisfies a threshold condition; and determining an output of the activation function based on the estimation value.

