Method and apparatus with neural network parameter quantization

1 . A processor-implemented method, the method comprising:

determining a first quantization value by performing log quantization on a parameter processed in a layer of a neural network;

comparing a threshold value with an error between a first dequantization value obtained by dequantization of the first quantization value and the parameter; and

quantizing the parameter into two or more quantization values including the first quantization value based on the result of the comparing to avoid degradation of the neural network.

2 . The method of claim 1 , wherein the determining of the first quantization value comprises:

determining the first quantization value by performing log quantization on a value corresponding to a quantization level closest to the parameter, from among a plurality of quantization levels.

3 . The method of claim 1 , wherein the quantizing of the parameter comprises:

determining a second quantization value by performing log quantization on the error in response to the error being greater than the threshold value as a result of the comparing; and

quantizing the parameter to a value in which the first quantization value and the second quantization value are grouped.

4 . The method of claim 3 , wherein the determining of the second quantization value comprises:

determining the second quantization value by performing log quantization on a value corresponding to a quantization level closest to the error, from among the plurality of quantization levels.

5 . The method of claim 3 , wherein the second quantization value is represented by a same number of bits as a number of bits representing the first quantization value.

6 . The method of claim 3 , wherein the quantizing comprises:

adding a tag bit to each of the first quantization value and the second quantization value.

7 . The method of claim 6 , wherein the adding comprises:

adding a first tag bit, indicating that there is the second quantization value subsequent to the first quantization value, before a first bit of bits representing the first quantization value or after a last bit of the bits; and

adding a second tag bit, indicating that there is no quantization value subsequent to the second quantization value, before a first bit of bits representing the second quantization value or after a last bit of the bits.

8 . The method of claim 3 , wherein the quantizing comprises:

adding a code value, indicating that the first quantization value and the second quantization value are consecutive values, before a first bit of bits representing the first quantization value or after a last bit of bits representing the second quantization value.

9 . The method of claim 3 , further comprising:

dequantizing the value in which the first quantization value and the second quantization value are grouped; and

performing a convolution operation between a dequantization value obtained by dequantizing the value and input activation values.

10 . The method of claim 9 , wherein the dequantizing of the value comprises:

calculating each of a first dequantization value, which is a value obtained by dequantization of the first quantization value, and a second dequantization value, which is a value obtained by dequantization of the second quantization value; and

obtaining the dequantization value by adding the first dequantization value and the second dequantization value.

11 . The method of claim 1 , wherein the threshold value is determined based on a predetermined trade-off relationship between a recognition rate of the neural network and a size of data according to the quantization of the parameter.

12 . A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1 .

13 . An apparatus, the apparatus comprising:

one or more processors configured to:

determine a first quantization value by performing log quantization on a parameter processed in a layer of a neural network;

compare a threshold value with an error between a first dequantization value obtained by dequantization of the first quantization value and the parameter; and

quantize the parameter into two or more quantization values including the first quantization value based on the result of the comparing to avoid degradation of the neural network.