Apparatuses and methods to accelerate matrix multiplication

An apparatus, comprising:

• a plurality of multipliers to compute products by multiplying integer values, the integer values corresponding to input data elements of a neural network operation;
• a plurality of shifters to operate in different modes for different data precisions, wherein the shifters operate in a floating-point mode to shift the products based on a maximum exponent of the products when the input data elements are floating-point data elements, wherein the shifters operate in an integer mode to bypass shifting the products when the input data elements are integer data elements;
• one or more adders to produce a sum from outputs of the shifters;
• and
• an accumulation unit to produce an output of the neural network operation from the sum and one or more other sums produced by the one or more adders.

An apparatus, comprising:

• a data storage unit to store input data elements of a neural network operation;
• a plurality of multipliers to compute products by multiplying integer values corresponding to the input data elements, the integer values represented by bits retrieved from the data storage unit;
• a plurality of shifters to operate in different modes for different data precisions, wherein the shifters operate in a floating-point mode to shift the products based on a maximum exponent of the products when the input data elements are floating-point data elements, wherein the shifters operate in an integer mode to bypass shifting the products when the input data elements are integer data elements;
• one or more adders to produce a sum from outputs of the shifters;
• and
• an accumulation unit to produce an output of the neural network operation from the sum and one or more other sums produced by the one or more adders.

An apparatus, comprising:

• a plurality of multipliers to compute products by multiplying integer values, the integer values corresponding to input data elements of a neural network operation;
• an exponent unit to operate in different modes for different data precisions, wherein the exponent unit determines a maximum exponent of the products in a floating-point mode and is bypassed in an integer mode;
• a plurality of shifters to operate in the different modes for the different data precisions, wherein the shifters operate in the floating-point mode to shift the products based on a maximum exponent of the products when the input data elements are floating-point data elements and operate in the integer mode to bypass shifting the products when the input data elements are integer data elements;
• one or more adders to produce a sum from outputs of the shifters;
• and
• an accumulation unit to produce an output of the neural network operation from the sum and one or more other sums produced by the one or more adders.