Methods and systems for determining a representative input data set for post-training quantization of artificial neural networks

Computer implemented method for determining a representative input data set for post-training quantization of artificial neural networks, the method comprising of the following steps:

• acquiring an input data set;
• processing the input data set to extract one or more parameters associated with the input data set, wherein metrics applied to the input data set to extract the one or more parameters are based on user input;
• based on the extracted one or more parameters, setting initial values for clipping limits comprising a lower clipping limit and an upper clipping limit, wherein the lower clipping limit and the upper clipping limit define threshold values for limiting a value range of the input data set;
• adapting the upper and lower clipping limits by computing a quantization error based on a numerical difference between the input data set and a quantized and a dequantized input data set, wherein the error is computed based on an error metric which is based on user input;
• and
• determining a representative input data set as a subset of the input data set whose elements fit within the clipping limits;
• wherein the representative input data set is to be used for quantization calibration of the post-training quantization.