Learned step size quantization

A method of training an artificial neural network, the method comprising:

• providing training input data to an artificial neural network, the artificial neural network comprising:
• a quantizer having a configurable step size, the quantizer adapted to receive a plurality of input values and quantize the plurality of input values according to the configurable step size to produce a plurality of quantized input values,
• at least one matrix multiplier configured to receive the plurality of quantized input values from the quantizer and to apply a plurality of weights to the quantized input values to determine a plurality of output values having a first precision, and
• a multiplier configured to scale the output values to a second precision;
• optimizing the configurable step size based on a gradient through the quantizer and the training input data.

A computer program product for training an artificial neural network, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:

• providing training input data to an artificial neural network, the artificial neural network comprising:
• a quantizer having a configurable step size, the quantizer adapted to receive a plurality of input values and quantize the plurality of input values according to the configurable step size to produce a plurality of quantized input values,
• at least one matrix multiplier configured to receive the plurality of quantized input values from the quantizer and to apply a plurality of weights to the quantized input values to determine a plurality of output values having a first precision, and
• a multiplier configured to scale the output values to a second precision;
• optimizing the configurable step size based on a gradient through the quantizer and the training input data.