Asymmetrical quantization of groups of data

A method for processing data, comprising:

• obtaining a group of data to be quantized for a machine learning model;
• quantizing the group of data to be quantized respectively through using a plurality of pairs of truncation thresholds to determine a plurality of groups of quantized data, wherein each pair of truncation thresholds in the plurality of pairs of truncation thresholds includes an upper truncation and a lower truncation, and the upper truncation and the lower truncation in at least one pair of truncation thresholds in the plurality of pairs of truncation thresholds have different absolute values;
• and
• selecting a pair of truncation thresholds from the plurality of pairs of truncation thresholds based on a difference between a mean value of an absolute value of each group of quantized data in the plurality of groups of quantized data and a mean value of an absolute value of the group of data to be quantized to quantize the group of data to be quantized.