Iterative reduction of quantization error in neural network model

A controlling method of an electronic device, the controlling method comprising:

• identifying, based on an iteration-related loss value, an iteration count of quantization for error data for each of a plurality of layers, based on first model information including information on the plurality of layers of a neural network model and first weight data expressed in a first bit-width;
• acquiring quantization information including second weight data corresponding to each of the first weight data and expressed in a second bit-width smaller than the first bit-width by performing recursive quantization according to the iteration count;
• and
• acquiring second model information on a reconfigured neural network model by reconfiguring the neural network model based on the first model information, the iteration count, and the second weight data,
• wherein the identifying of the iteration count includes:
• acquiring temporary weight data expressed in the first bit-width by performing the recursive quantization on the first weight data according to an initial iteration count;
• acquiring a first loss value by performing a feedforward process of the neural network model based on the temporary weight data;
• acquiring a second loss value for latency of hardware by performing a simulation on the hardware for driving the neural network model based on the temporary weight data;
• and
• identifying the iteration count by updating a current iteration count such that a third loss value is decreased, wherein the third loss value is a combination of the first loss value and the second loss value, wherein the third loss value is the iteration-related loss value,
• wherein the acquiring of the temporary weight data comprises:
• acquiring first temporary weight data expressed in the first bit-width by performing first quantization including a quantization and an inverse quantization of the first weight data expressed in the first bit-width,
• acquiring first error data expressed in the first bit-width based on the first weight data and the first temporary weight data expressed in the first bit-width,
• acquiring second error data expressed in the second bit-width by performing second quantization including a quantization and an inverse quantization of the first error data expressed in the first bit-width,
• determining, as the temporary weight data, second temporary weight data by summing the first temporary weight data, the first error data, and the second error data,
• acquiring third temporary weight data expressed in the first bit-width by performing the recursive quantization on the first weight data according to a first iteration count acquired by rounding down the current iteration count, and acquiring fourth temporary weight data expressed in the first bit-width by performing the recursive quantization on the first weight data according to a second iteration count acquired by rounding up the current iteration count, when the iteration count is not an integer;
• interpolating weights corresponding to each other among the third temporary weight data and the fourth temporary weight data;
• and
• determining temporary weight data acquired according to a result of the interpolation as the second temporary weight data.

A non-transitory computer readable recording medium including a program for executing a controlling method of an electronic device, wherein the controlling method of the electronic device includes:

• identifying an iteration count of quantization for error data for each of a plurality of layers, based on first model information and an iteration-related loss value, including information on the plurality of layers of a neural network model and first weight data expressed in a first bit-width;
• acquiring quantization information including second weight data corresponding to each of the first weight data and expressed in a second bit-width smaller than the first bit-width by performing recursive quantization according to the iteration count;
• and
• acquiring second model information on a reconfigured neural network model by reconfiguring the neural network model based on the first model information, the iteration count, and the second weight data,
• wherein the identifying of the iteration count includes:
• acquiring temporary weight data expressed in the first bit-width by performing the recursive quantization on the first weight data according to an initial iteration count;
• acquiring a first loss value by performing a feedforward process of the neural network model based on the temporary weight data;
• acquiring a second loss value for latency of hardware by performing a simulation on the hardware for driving the neural network model based on the temporary weight data;
• and
• identifying the iteration count by updating a current iteration count such that a third loss value is decreased, wherein the third loss value is a combination of the first loss value and the second loss value, wherein the third loss value is the iteration-related loss value,
• wherein the acquiring of the temporary weight data comprises:
• acquiring first temporary weight data expressed in the first bit-width by performing first quantization including a quantization and an inverse quantization of the first weight data expressed in the first bit-width,
• acquiring first error data expressed in the first bit-width based on the first weight data and the first temporary weight data expressed in the first bit-width,
• acquiring second error data expressed in the second bit-width by performing second quantization including a quantization and an inverse quantization of the first error data expressed in the first bit-width,
• determining, as the temporary weight data, second temporary weight data by summing the first temporary weight data, the first error data, and the second error data,
• acquiring third temporary weight data expressed in the first bit-width by performing the recursive quantization on the first weight data according to a first iteration count acquired by rounding down the current iteration count, and acquiring fourth temporary weight data expressed in the first bit-width by performing the recursive quantization on the first weight data according to a second iteration count acquired by rounding up the current iteration count, when the iteration count is not an integer;
• interpolating weights corresponding to each other among the third temporary weight data and the fourth temporary weight data;
• and
• determining temporary weight data acquired according to a result of the interpolation as the second temporary weight data.