WO2024135861A1 - Procédé d'entraînement de réseau d'apprentissage profond appliquant un type de représentation de données variable, et dispositif mobile l'appliquant - Google Patents
Procédé d'entraînement de réseau d'apprentissage profond appliquant un type de représentation de données variable, et dispositif mobile l'appliquant Download PDFInfo
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- WO2024135861A1 WO2024135861A1 PCT/KR2022/020665 KR2022020665W WO2024135861A1 WO 2024135861 A1 WO2024135861 A1 WO 2024135861A1 KR 2022020665 W KR2022020665 W KR 2022020665W WO 2024135861 A1 WO2024135861 A1 WO 2024135861A1
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- 238000013135 deep learning Methods 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000012549 training Methods 0.000 title claims abstract description 13
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000013136 deep learning model Methods 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 description 5
- 238000013139 quantization Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000007667 floating Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000007786 learning performance Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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- the present invention relates to deep learning learning, and more specifically, to a method of additionally learning and re-learning a model that has been trained on a server on a mobile device.
- re-learning In order to operate a model that has been trained on a server on a new device, re-learning must be performed to regenerate deep learning parameters using the data used for learning and the data used for testing.
- the present invention was created to solve the above problems, and the purpose of the present invention is to provide a method for additional learning or relearning in mobile devices with limited memory resources, and a deep learning computing device and method using variable data phenotypes. In providing.
- a deep learning network learning method includes the steps of setting a data phenotype for each layer constituting a deep learning network; It includes the step of training a deep learning network while changing the phenotype of the data input to each layer according to the set data phenotype.
- the setting step may be setting the number of bits in the exponent part and setting the number of bits in the mantissa part for each layer.
- the setting step may be setting the data representation for each channel for each layer.
- the deep learning network training method according to the present invention may further include quantizing a training dataset to be used for deep learning network training.
- the learning step may be to modify the phenotype of the data between layers from the phenotype of the previous layer to the phenotype of the next layer.
- the setting step may be setting the data expression for each layer based on the data expression received from the outside.
- Data phenotype can be determined based on the importance of each layer.
- a control unit that sets data phenotypes for each layer constituting a deep learning network; and a data conversion unit that changes the phenotype of data input to each layer according to the data phenotype set by the control unit.
- a deep learning network device comprising a deep learning accelerator that trains a deep learning network with data whose phenotype is changed by a data conversion unit.
- variable data phenotypes for each layer and channel when performing additional learning or relearning, there is a significant reduction in accuracy in mobile devices with limited memory resources and power. It is possible to perform additional learning and retraining of the deep learning model without it.
- 1 is a diagram showing the inference process (forward path) in the learning process
- Figure 2 is a diagram showing the backpropagation process (Backward path) in the learning process
- Figure 6 shows learning performance using data suggested phenotypes
- Figure 7 is a mobile device according to an embodiment of the present invention.
- FP16 which usually uses fewer bits, is used, or hardware is configured using fixed points to reduce the size of the operator.
- this also has the disadvantage of slowing the learning speed due to bandwidth problems occurring when inputting and outputting data to and from external memory, and lowering the learning accuracy due to the low accuracy of the calculator.
- an embodiment of the present invention presents a deep learning learning method applying variable data phenotypes.
- This is a technology that processes data (storage, calculation, transmission, etc.) by changing it into a flexible data format according to a specific distribution so that various phenotypes (fixed point, floating point, etc.) shown in Figure 4 can be applied for learning data processing and calculation.
- the data expression can be set differently for each layer. By distinguishing between important and non-important layers, the data expression can be set differently.
- data expression is set for each channel.
- the data phenotype can be set differently for each channel. By distinguishing between important channels and unimportant channels, the data phenotype can be set differently.
- Figure 5 shows variable data phenotypes that can be set.
- PEE Partial Exponent Expression
- Revised FP represents the mantissa part
- the data expression can be set by flexibly changing the number of bits in both the exponent part and the mantissa part.
- the data phenotype can be set/controlled by receiving the data phenotype set from an external source, such as the host.
- Revised FP size of Revised FP can be changed to a variety of bits depending on hardware resources.
- Revised FP if the operation is performed with an existing fixed point, all bits are considered as mantissa, and when processing with FP operation, small exponent expression and mantissa are used. It can be expressed by dividing it into .
- the deep learning network is trained by changing the phenotype of the data input to each layer according to the set data phenotype, that is, changing the phenotype of the data between layers from the phenotype of the previous layer to the phenotype of the next layer.
- the method according to the embodiment of the present invention is mainly used in the low-bit or data pre-processing process during hardware processing, it can operate similarly to existing data input by adding simple hardware logic, resulting in performance similar to original-level learning. It is possible to derive
- memory usage can be further reduced by using the minimum number of bits in the learning process, and the intermediate data storage space for the learning data can be used as additional batch data. It is also possible to increase the size.
- FIG. 7 is a block diagram of a mobile device according to an embodiment of the present invention.
- a mobile device according to an embodiment of the present invention is configured to include a memory 110, an MCU 120, a deep learning accelerator 130, a quantization unit 140, and a data conversion unit 150, as shown. .
- the MCU 120 receives some learning data from the server, stores it in the memory 110, and sets the data phenotype for each layer/channel of the deep learning network.
- the quantization unit 140 performs quantization on the training data stored in the memory 110, and the data conversion unit 150 converts the quantized training data into a phenotype set in each layer/channel.
- the deep learning accelerator 130 further trains or retrains the deep learning model learned on the server using learning data that is quantized by the quantization unit 140 and then converted into phenotype by the data conversion unit 150.
- variable data phenotypes are used for each layer/channel. It was made applicable.
- a computer-readable recording medium can be any data storage device that can be read by a computer and store data.
- computer-readable recording media can be ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical disk, hard disk drive, etc.
- computer-readable codes or programs stored on a computer-readable recording medium may be transmitted through a network connected between computers.
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Abstract
Sont proposés un procédé d'entraînement de réseau d'pprentissage profond appliquant un type de représentation de données variable, et un dispositif mobile l'appliquant. Le procédé d'entraînement de réseau d'apprentissage profond selon un mode de réalisation de la présente invention consiste à : configurer un type de représentation de données pour chacune de couches constituant un réseau d'apprentissage profond ; et entraîner le réseau d'apprentissage profond tout en changeant un type de représentation de données, qui est entré dans chaque couche, en fonction du type de représentation de données configuré. Par conséquent, en appliquant un type de représentation de données variable pour chaque couche et chaque canal lors de la réalisation d'un apprentissage supplémentaire ou d'un réapprentissage, il est possible de réaliser un apprentissage supplémentaire et un réapprentissage pour un modèle d'apprentissage profond sur un dispositif mobile avec des ressources de mémoire et une puissance limitées sans réduction significative de la précision.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR1020220177737A KR20240096949A (ko) | 2022-12-19 | 가변 데이터 표현형을 적용한 딥러닝 학습 방법 및 이를 적용한 모바일 디바이스 | |
KR10-2022-0177737 | 2022-12-19 |
Publications (1)
Publication Number | Publication Date |
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WO2024135861A1 true WO2024135861A1 (fr) | 2024-06-27 |
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PCT/KR2022/020665 WO2024135861A1 (fr) | 2022-12-19 | 2022-12-19 | Procédé d'entraînement de réseau d'apprentissage profond appliquant un type de représentation de données variable, et dispositif mobile l'appliquant |
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WO (1) | WO2024135861A1 (fr) |
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2022
- 2022-12-19 WO PCT/KR2022/020665 patent/WO2024135861A1/fr unknown
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