Granted Patent B2

US 12,602,749 · App. 18/408,601 · Granted Apr 14, 2026

Inventor: Chuan-Yue Yang (New Taipei City, TW)

Assignee: GENESYS LOGIC, INC.

G06T5/60G06T2207/20016G06T2207/20081G06T2207/20084

Abstract

Claims (60)

1 . A parameter optimizing method of a neural network, comprising:

obtaining a shared filter, wherein the shared filter comprises a plurality of shared weights;

assigning the shared weights to a plurality of sub-filters, wherein each of the sub-filters corresponds to one of a plurality of channels, each of the sub-filters comprises a plurality of sub-weights, and a size of each of the sub-filters is smaller than or equal to a size of the shared filter;

generating the sub-weights of each of the sub-filters according to assigned shared weights; and

performing a computation with at least one input data by using the sub-filters of the channels respectively.

2 . The parameter optimizing method of a neural network according to claim 1 , wherein assigning the shared weights to the sub-filters comprises:

defining a plurality of areas of the shared filter according to the size of each of the sub-filters, wherein a number of the sub-weights in each of the sub-filters is the same as a number of the areas, a plurality of shared weights located in one area have the same value, and the shared weights in each of the areas have different values;

mapping the sub-filters to the shared filter to generate a mapping result, wherein the mapping result comprises the area of the shared filter to which each of the sub-weights of each of the sub-filters corresponds; and

assigning the shared weights according to the mapping result, wherein each selected sub-weight of a corresponding selected area of each of the sub-filters corresponds to at least one of the shared weights in the area mapped.

3 . The parameter optimizing method of a neural network according to claim 2 , wherein generating the sub-weights of each of the sub-filters according to the assigned shared weights comprises:

for each of the sub-filters, obtaining the sub-weights by adding up the corresponding shared weights in the area of the shared filter to which each selected sub-weight in each selected area is mapped.

4 . The parameter optimizing method of a neural network according to claim 2 , wherein the sub-filters comprise a first sub-filter and a second sub-filter, and mapping the sub-filters to the shared filter comprises:

setting at least one of the shared weights of the shared filter mapped to a selected area of the first sub-filter to be different from at least one of the shared weights of the shared filter mapped to a selected area of the second sub-filter.

5 . The parameter optimizing method of a neural network according to claim 1 , wherein the computation is a first convolution operation, the at least one input data comprises a plurality of pieces of first input data corresponding to the channels, and performing the computation with the at least one input data by using the sub-filters of the channels respectively comprises:

performing the first convolution operation on one of the sub-filters and one piece of the first input data respectively according to the corresponding channel to generate a plurality of pieces of first output data corresponding to the channels; and

performing a first format conversion on the first output data to generate second output data of a single channel.

6 . The parameter optimizing method of a neural network according to claim 5 , wherein the number of the channels is N, and performing the first format conversion on the first output data comprises:

assigning first elements at the same position in the pieces of first output data to N adjacent elements in the second output data.

7 . The parameter optimizing method of a neural network according to claim 5 , wherein before performing the first convolution operation on one of the sub-filters and one piece of the first input data respectively according to the corresponding channel, the parameter optimizing method further comprises:

performing a second format conversion on a first image to generate the first input data corresponding to the channels.

8 . The parameter optimizing method of a neural network according to claim 5 , wherein before performing the first convolution operation on one of the sub-filters and one piece of the first input data respectively according to the corresponding channel, the parameter optimizing method further comprises:

performing a first encoding on a second image to generate the first input data corresponding to the channels, wherein the first encoding comprises an average pooling or a second format conversion for conversion from a single channel to multiple channels.

9 . The parameter optimizing method of a neural network according to claim 5 , wherein before performing the first convolution operation on one of the sub-filters and one piece of the first input data respectively according to the corresponding channel, the parameter optimizing method further comprises:

performing at least a second convolution operation on a third image to generate the first input data corresponding to the channels.

10 . The parameter optimizing method of a neural network according to claim 1 , wherein the computation is a first convolution operation, the at least one input data comprises second input data of a single channel, and performing the computation with the at least one input data by using the sub-filters of the channels respectively comprises:

performing the first convolution operation on the sub-filters and the second input data respectively to generate a plurality of pieces of third output data corresponding to the channels; and

performing a first format conversion on the third output data to generate fourth output data of a single channel.

11 . The parameter optimizing method of a neural network according to claim 1 , wherein after performing the computation with the at least one input data by using the sub-filters of the channels respectively, the parameter optimizing method further comprises:

generating output data, wherein the input data has a first resolution, the output data has a second resolution, and the second resolution is higher than the first resolution, and the size of the shared filter is the same as a size of the output data.

12 . A computing apparatus, comprising:

a storage configured to store a program code; and

a processor coupled to the storage and configured to load and execute the program code to:

obtain a shared filter, wherein the shared filter comprises a plurality of shared weights;

assign the shared weights to a plurality of sub-filters, wherein each of the sub-filters corresponds to one of a plurality of channels, each of the sub-filters comprises a plurality of sub-weights, and a size of each of the sub-filters is smaller than or equal to a size of the shared filter;

generate the sub-weights of each of the sub-filters according to the assigned shared weights; and

perform a computation with at least one input data by using the sub-filters of the channels respectively.

13 . The computing apparatus according to claim 12 , wherein the processor is further configured to:

define a plurality of areas of the shared filter according to the size of each of the sub-filters, wherein a number of the sub-weights in each of the sub-filters is the same as a number of the areas, a plurality of shared weights located in one area have the same value, and the shared weights in each of the areas have different values;

map the sub-filters to the shared filter to generate a mapping result, wherein the mapping result comprises the area of the shared filter to which each of the sub-weights of each of the sub-filters corresponds; and

assign the shared weights according to the mapping result, wherein each selected sub-weight of a corresponding selected area of each of the sub-filters corresponds to at least one of the shared weights in the area mapped.

14 . The computing apparatus according to claim 13 , wherein the processor is further configured to:

for each of the sub-filters, obtain the sub-weights by adding up the corresponding shared weights in the area of the shared filter to which each selected sub-weight in each selected area is mapped.

15 . The computing apparatus according to claim 13 , wherein the sub-filters comprise a first sub-filter and a second sub-filter, and the processor is further configured to:

set at least one of the shared weights of the shared filter mapped to a selected area of the first sub-filter to be different from at least one of the shared weights of the shared filter mapped to a selected area of the second sub-filter.

16 . The computing apparatus according to claim 12 , wherein the computation is a first convolution operation, the at least one input data comprises a plurality of pieces of first input data corresponding to the channels, and the processor is further configured to:

perform the first convolution operation on one of the sub-filters and one piece of the first input data respectively according to the corresponding channel to generate a plurality of pieces of first output data corresponding to the channels; and

perform a first format conversion on the first output data to generate second output data of a single channel.

17 . The computing apparatus according to claim 16 , wherein the number of the channels is N, and the processor is further configured to:

assign first elements at the same position in the pieces of first output data to N adjacent elements in the second output data.

18 . The computing apparatus according to claim 16 , wherein the processor is further configured to:

perform a second format conversion on a first image to generate the first input data corresponding to the channels.

19 . The computing apparatus according to claim 16 , wherein the processor is further configured to:

perform a first encoding on a second image to generate the first input data corresponding to the channels, wherein the first encoding comprises an average pooling or a second format conversion for conversion from a single channel to multiple channels.

20 . The computing apparatus according to claim 16 , wherein the processor is further configured to:

perform at least a second convolution operation on a third image to generate the first input data corresponding to the channels.

21 . The computing apparatus according to claim 12 , wherein the computation is a first convolution operation, the at least one input data comprises second input data of a single channel, and the processor is further configured to:

perform the first convolution operation on the sub-filters and the second input data respectively to generate a plurality of pieces of third output data corresponding to the channels; and

perform a first format conversion on the third output data to generate fourth output data of a single channel.

22 . The computing apparatus according to claim 12 , wherein the processor is further configured to:

generate output data, wherein the input data has a first resolution, the output data has a second resolution, and the second resolution is higher than the first resolution, and the size of the shared filter is the same as a size of the output data.

Assignments (1)

Priority Claims (1)

Continuity (2)

References Cited (12)