Quantization method and device for weights of batch normalization layer

A batch normalization layer quantization device for performing a quantization, comprising:

• an input part that receives a plurality of previously-learned first weights of a plurality of batch normalization layers implemented in hardware in a neural network, and input data of the plurality of batch normalization layers;
• a processor that, for the plurality of batch normalization layers, performs operations for quantizing weights of the plurality of batch normalization layers to reduce a bit-width requirement amount and to reduce a memory capacity required to store the weights, the operations including:
• obtaining first distribution information of the plurality of previously-learned first weights;
• performing a first quantization on the plurality of previously-learned first weights using the first distribution information to obtain a plurality of second weights;
• assigning a first bit width, which is a part of all bit widths assigned to the quantization, to the first quantization;
• obtaining second distribution information of the second plurality of weights;
• assigning a second bit width, which is a part of all bit widths assigned to the quantization, to a second quantization;
• performing the second quantization on the plurality of second weights using the second distribution information to obtain a plurality of final weights having the first bit width and the second bit width;
• and
• performing normalization on the input data using the plurality of final weights;
• and
• a memory that stores the plurality of final weights using the first bit width and the second bit width;
• wherein the first bit width and the second bit width are a same bit width that is reduced from bit widths of the previously-learned first weights before the quantization.

A quantization method for performing a quantization, comprising:

• for a plurality of batch normalization layers implemented in hardware in a neural network, performing operations for quantizing weights of the plurality of batch normalization layers to reduce a bit-width requirement amount and to reduce a memory capacity required to store the weights, the operations including:
• receiving a plurality of previously-learned first weights of the plurality of batch normalization layers;
• obtaining first distribution information of the plurality of previously-learned first weights;
• performing a first quantization on the plurality of previously-learned first weights using the first distribution information to obtain a plurality of second weights;
• assigning a first bit width, which is a part of all bit widths assigned to the quantization, to the first quantization;
• obtaining second distribution information of the plurality of second weights;
• assigning a second bit width, which is a part of all bit widths assigned to the quantization, to a second quantization;
• performing the second quantization on the plurality of second weights using the second distribution information to obtain a plurality of final weights having the first bit width and the second bit width;
• and
• performing normalization on an input data using the plurality of final weights;
• wherein the first bit width and the second bit width are a same bit width that is reduced from bit widths of the previously-learned first weights before the quantization.