Iterative reduction of quantization error in neural network model

Abstract

An electronic device and a controlling method of an electronic device are provided. An electronic device recursively determines a plurality of layers of a neural network model. Weight data of first model information is recursively quantized to obtain a second neural network model. The recursive quantization begins with the weight data and determines an iteration count of a recursion. The recursion operates on error data, quantized weight data, scale data and quantized error data to obtain the iteration count. A first bit-width of the weight data is reduced to a second bit-width of the quantized weight data. The recursion may be performed on a per-layer basis. The weight data may be formulated in a floating-point format and the quantized weight data may be formulated in a fixed point format with an integer number of bits.