Dynamic precision management for integer deep learning primitives

A method comprising:

• receiving a set of dynamic fixed-point tensors;
• computing a right-shift value using an absolute maximum value within the set of dynamic fixed-point tensors and a dynamic range of the set of dynamic fixed-point tensors;
• right-shifting data values within the set of dynamic fixed-point tensors;
• incrementing a shared exponent associated with the set of dynamic fixed-point tensors based on an amount of right-shift applied to the data values within the set of dynamic fixed-point tensors;
• and
• performing a compute operation on the set of dynamic fixed-point tensors.

A data processing system comprising:

• a memory to store a set of dynamic fixed-point tensors;
• and
• one or more processors including at least one graphics processor, the at least one graphics processor including a compute unit including a hardware logic unit having dynamic precision fixed-point logic, the compute unit to:
• receive the set of dynamic fixed-point tensors;
• compute, via the dynamic precision fixed-point logic, a right-shift value using an absolute maximum value within the set of dynamic fixed-point tensors and a dynamic range of the set of dynamic fixed-point tensors;
• right-shift data values within the set of dynamic fixed-point tensors based on the right-shift value;
• increment a shared exponent associated with the set of dynamic fixed-point tensors based on the right-shift value;
• perform a compute operation on the set of dynamic fixed-point tensors;
• and
• generate an output tensor via the compute operation on the set of dynamic fixed-point tensors.