Quantization method and device for weights of batch normalization layer
An embodiment of the present invention provides a quantization method for weights of a plurality of batch normalization layers, including: receiving a plurality of previously learned first weights of the plurality of batch normalization layers; obtaining first distribution information of the plurality of first weights; performing a first quantization on the plurality of first weights using the first distribution information to obtain a plurality of second weights; obtaining second distribution information of the plurality of second weights; and performing a second quantization on the plurality of second weights using the second distribution information to obtain a plurality of final weights, and thereby reducing an error that may occur when quantizing the weight of the batch normalization layer.
1. 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;
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.
2. The quantization method of claim 1 , wherein
the first bit width and the second bit width are 4 bits.
3. The quantization method of claim 1 , wherein the first distribution information includes an average value and a variance value of the plurality of previously-learned first weights, and the second distribution information includes an average value and a variance value of the plurality of second weights.
4. The quantization method of claim 1 , wherein
the first quantization is an integer power-of-two quantization, and
the second quantization is a dynamic range floating point quantization.
5. The quantization method of claim 1 , further comprising repeating the receiving, the obtaining of the first distribution information, and the first quantizing, for the plurality of previously-learned first weights a predetermined number of times.
6. The quantization method of claim 1 , further comprising
repeatedly applying a quantization process for a first layer of remaining layers among the plurality of batch normalization layers.
7. 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.
8. The batch normalization layer quantization device of claim 7 , wherein the first bit width and the second bit width are 4 bits.
9. The batch normalization layer quantization device of claim 7 , wherein
the first quantization is an integer power-of-two quantization, and
the second quantization is a dynamic range floating point quantization.
10. The batch normalization layer quantization device of claim 7 , wherein the first distribution information includes an average value and a variance value of the plurality of previously-learned first weights, and the second distribution information includes an average value and a variance value of the plurality of second weights.
11. The batch normalization layer quantization device of claim 7 , wherein the processor repeats the receiving, the obtaining of the first distribution information, and the first quantizing, for the plurality of previously-learned first weights a predetermined number of times.
12. 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.
13. The quantization method of claim 12 , wherein the first distribution information includes an average value and a variance value of the plurality of previously-learned first weights, and the second distribution information includes an average value and a variance value of the plurality of second weights.
14. The quantization method of claim 13 , further comprising
repeatedly applying a quantization process for a first layer of remaining layers among the plurality of batch normalization layers.