# What are the applications in which the precision of the neural network's weights is unimportant?

While reading about Module in PyTorch, I came across a new data type called half datatype.

half() method when calls on a Module casts all floating-point parameters and buffers to half datatype.

It is a 16-bit floating-point number as mentioned here.

It is mentioned in Wikipedia that

It is intended for storage of floating-point values in applications where higher precision is not essential for performing arithmetic computations.

It implies that the precision of parameters (say, weights for a neural network) is not important in certain applications and hence one can use half datatype while implementing a neural network.

Did any research support the statement that precision, that is the range of values it takes, of weights, is unimportant for certain applications?