Value-dependent quantization for machine learning

A computing system for quantizing linear operations of machine learning computations, the computing system comprising:

• one or more hardware computing processors;
• a field size bounding engine executable by the one or more hardware computing processors and configured to determine a maximum field size for a field containing values of a quantized version of a weight matrix, a quantized version of an input data matrix, and a quantized version of a bias matrix;
• a scaling factor bounding engine executable by the one or more hardware computing processors and configured to determine a weight upper bound of scaling factors for the weight matrix based on values of elements of the weight matrix and the maximum field size, an input data upper bound of scaling factors for the input data matrix based on values of elements of the input data matrix and the maximum field size, and a bias upper bound of scaling factors for the bias matrix based on values of elements of the bias matrix and the maximum field size;
• and
• a scaling factor assigner executable by the one or more hardware computing processors and configured to set a weight scaling factor of the weight matrix to the weight upper bound, an input data scaling factor of the input data matrix to the input data upper bound, and a bias scaling factor for the bias matrix to a sum of the weight upper bound and the input data upper bound, when the sum is less than or equal to the bias upper bound and to set the bias scaling factor for the bias matrix to the bias upper bound and the weight scaling factor of the weight matrix and the input data scaling factor of the input data matrix based on the bias upper bound, when the sum is greater than the bias upper bound.

One or more tangible processor-readable storage media embodied with instructions for executing on one or more processors and circuits of a computing device a process for quantizing linear operations of machine learning computations, the process comprising:

• determining a maximum field size for a field containing values of a quantized version of a weight matrix, a quantized version of an input data matrix, and a quantized version of a bias matrix;
• determining a weight upper bound of scaling factors for the weight matrix based on values of elements of the weight matrix and the maximum field size, an input data upper bound of scaling factors for the input data matrix based on values of elements of the input data matrix and the maximum field size, and a bias upper bound of scaling factors for the bias matrix based on values of elements of the bias matrix and the maximum field size;
• setting a weight scaling factor of the weight matrix to the weight upper bound, an input data scaling factor of the input data matrix to the input data upper bound, and a bias scaling factor for the bias matrix to a sum of the weight upper bound and the input data upper bound, when the sum is less than or equal to the bias upper bound;
• and
• setting the bias scaling factor for the bias matrix to the bias upper bound and the weight scaling factor of the weight matrix and the input data scaling factor of the input data matrix based on the bias upper bound, when the sum is greater than the bias upper bound.