Abstract: A method that may be computer implemented converts a tensor value from a first format to a second format and trains a neural network. The method determines a maximum exponent code in the first format and subtracts a first bias to obtain the highest needed exponent. It determines a second bias from the highest available code (HAC) in the second format and the HNE, and converts the tensor value from the first format to the second format by using the second bias instead of the first bias. The method uses the second format to train the neural network. The method may round the mantissa of the tensor value in the first format to obtain a rounded mantissa of the tensor value for the second format.

Abstract: Systems, apparatuses and methods may provide for technology that in response to an identification that one or more hardware units are to execute on a first type of data format, decomposes a first original floating point number to a plurality of first segmented floating point numbers that are to be equivalent to the first original floating point number. The technology may further in response to the identification, decompose a second original floating point number to a plurality of second segmented floating point numbers that are to be equivalent to the second original floating point number. The technology may further execute a multiplication operation on the first and second segmented floating point numbers to multiply the first segmented floating point numbers with the second segmented floating point numbers.

Abstract: Systems, apparatuses and methods may provide for technology that in response to an identification that one or more hardware units are to execute on a first type of data format, decomposes a first original floating point number to a plurality of first segmented floating point numbers that are to be equivalent to the first original floating point number. The technology may further in response to the identification, decompose a second original floating point number to a plurality of second segmented floating point numbers that are to be equivalent to the second original floating point number. The technology may further execute a multiplication operation on the first and second segmented floating point numbers to multiply the first segmented floating point numbers with the second segmented floating point numbers.