Convolutional neural network system having binary parameter and operation method thereof

Patent metadata

Field	Value
Publication number	US-2018197084-A1
Application number	US-201815866351-A
Country	US
Kind code	A1
Filing date	Jan 9, 2018
Priority date	Jan 11, 2017
Publication date	Jul 12, 2018
Grant date	—

Abstract

Provided is a convolutional neural network system. The system includes an input buffer configured to store an input feature, a parameter buffer configured to store a learning parameter, a calculation unit configured to perform a convolution layer calculation or a fully connected layer calculation by using the input feature provided from the input buffer and the learning parameter provided from the parameter buffer, and an output buffer configured to store an output feature outputted from the calculation unit and output the stored output feature to the outside. The parameter buffer provides a real learning parameter to the calculation unit at the time of the convolution layer calculation and provides a binary learning parameter to the calculation unit at the time of the fully connected layer calculation.

First claim

What is claimed is:

1 . A convolutional neural network system comprising:
an input buffer configured to store an input feature; a parameter buffer configured to store a learning parameter; a calculation unit configured to perform a convolution layer calculation or a fully connected layer calculation by using the input feature provided from the input buffer and the learning parameter provided from the parameter buffer; and an output buffer configured to store an output feature outputted from the calculation unit and output the stored output feature to the outside, wherein the parameter buffer provides a real learning parameter to the calculation unit at the time of the convolution layer calculation and provides a binary learning parameter to the calculation unit at the time of the fully connected layer calculation.

2 . The system of claim 1 , wherein the binary learning parameter has a data value of either ‘−1’ or ‘1’.

3 . The system of claim 2 , wherein the binary learning parameter is generated by mapping a value equal to or greater than ‘0’ to ‘1’ and mapping a value less than ‘0’ to ‘−1’ among real weights of the fully connected layer determined through learning.

4 . The system of claim 1 , wherein the calculation unit comprises:
a plurality of bit conversion logics configured to multiply each of the plurality of input features by the corresponding binary learning parameter to be outputted as a logic value at the time of the fully connected layer calculation; and an addition tree configured to add outputs of the plurality of bit conversion logics.

5 . The system of claim 4 , wherein each of the plurality of bit conversion logics converts each of the input features to binary data and multiplies the binary learning parameter by the converted binary data to deliver a result thereof to the addition tree.

6 . The system of claim 5 , wherein when the binary learning parameter is a logic ‘−1’, the binary learning parameter is converted in a 2's complement form of a corresponding input feature and deliver a result thereof to the addition tree.

7 . The system of claim 6 , wherein when the binary learning parameter is a logic ‘−1’, each of the plurality of bit conversion logics converts each of the input features to 1's complement and delivers a result thereof to the addition tree and the addition tree adds a count value of a logic ‘−1’ among the binary learning parameters.

8 . The system of claim 1 , wherein the calculation unit comprises:
a plurality of node calculation elements configured to sequentially process at least two input features among input features of the same layer at the time of the fully connected layer calculation according to a corresponding binary learning parameter; an addition logic configured to add output values of the node calculation elements; and a normalization block configured to normalize an output of the addition logic by referring to a mean and a variance of a batch unit.

9 . The system of claim 8 , wherein each of the plurality of node calculation elements comprises:
a bit conversion logic configured to convert each of the at least two input features to binary data and multiply each converted binary data by the corresponding binary learning parameter to sequentially output a result thereof; and an adder-register unit configured to accumulate at least two binary data outputted sequentially from the bit conversion logic.

10 . The system of claim 9 , wherein the calculation unit further comprises a weight decoder configured to convert the binary learning parameter to a logic ‘0’ or a logic ‘1’ before supplying the binary learning parameter to each of the plurality of node calculation elements.

11 . An operation method of a convolutional neural network system, the method comprising:
determining a real learning parameter through learning of the convolutional neural network system; converting a weight of a fully connected layer of the convolutional neural network system in the real learning parameter to a binary learning parameter; processing an input feature through a convolution layer calculation applying the real learning parameter; and processing a result of the convolution layer calculation through a fully connected layer calculation applying the binary learning parameter.

12 . The method of claim 11 , wherein the binary learning parameter is converted to have a data value of either ‘−1’ or ‘1’.

13 . The method of claim 12 , wherein the processing through the fully connected layer calculation comprises converting inputted real data to binary data and multiplying the converted binary data by the binary learning parameter to output a result thereof.

14 . The method of claim 13 , wherein the calculation of multiplying the binary data by the binary learning parameter ‘−1’ comprises a conversion calculation with 2's complement of the binary data.

15 . The method of claim 14 , wherein the calculation of multiplying the binary data by the binary leaning parameter ‘−1’ comprises a calculation of converting the binary data to 1's complement and adding to the 1's complement by the number of the binary learning parameters ‘−1’.

Assignees

• Electronics & Telecommunications Res Inst

Inventors

• Kim Ju-Yeob
• Kim Byung Jo
• Kim Jin Kyu
• Lee Mi Young
• Kim Seong Min
• Lee Joo Hyun

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0495

  Quantised networks; Sparse networks; Compressed networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title