International Business Machines Corporation

Deepika Bablani

Dharmendra S Modha

Rathinakumar Appuswamy

Steve Kyle Esser

Jeffrey L McKinstry

Learned step size quantization in artificial neural network is provided. In various embodiments, a system comprises an artificial neural network and a computing node. The artificial neural network comprises: 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. The computing node is operatively coupled to the artificial neural network and is configured to: provide training input data to the artificial neural network, and optimize the configurable step size based on a gradient through the quantizer and the training input data.

Growing community of inventors

San Jose, CA, United States of America

Deepika Bablani