International Business Machines Corporation

Zhuo Wang

Kailash Gopalakrishnan

Jungwook Choi

Swagath Venkataramani

Pierce I-Jen Chuang

Charbel Sakr

Techniques for statistics-aware weight quantization are presented. To facilitate reducing the bit precision of weights, for a set of weights, a quantizer management component can estimate a quantization scale value to apply to a weight as a linear or non-linear function of the mean of a square of a weight value of the weight and the mean of an absolute value of the weight value, wherein the quantization scale value is determined to have a smaller quantization error than all, or at least almost all, other quantization errors associated with other quantization scale values. A quantizer component applies the quantization scale value to symmetrically and/or uniformly quantize weights of a layer of the set of weights to generate quantized weights, the weights being quantized using rounding. The respective quantized weights can be used to facilitate training and inference of a deep learning system.

Statistics-aware weight quantization

Techniques that facilitate improving an efficiency of a neural network are described. In one embodiment, a system is provided that comprises a memory that stores computer-executable components and a processor that executes computer-executable components stored in the memory. In one implementation, the computer-executable components comprise an initialization component that selects an initial value of an output limit, wherein the output limit indicates a range for an output of an activation function of a neural network. The computer-executable components further comprise a training component that modifies the initial value of the output limit during training to a second value of the output limit, the second value of the output limit being provided as a parameter to the activation function. The computer-executable components further comprise an activation function component that determines the output of the activation function based on the second value of the output limit as the parameter.

Growing community of inventors

Mountain View, CA, United States of America

Zhuo Wang