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公开(公告)号：US20230186154A1
公开(公告)日：2023-06-15
申请号：US17893628
申请日：2022-08-23
Inventor： Byunghyun YOO , Hyun Woo KIM , Jeon Gue PARK , Hwa Jeon SONG , Jeongmin YANG , Sungwon YI , Euisok CHUNG , Ran HAN
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: An exploration method used by an exploration apparatus in multi-agent reinforcement learning to collect training samples during the training process is provided. The exploration method includes calculating the influence of a selected action of each agent on the actions of other agents in a current state, calculating a linear sum of the value of a utility function representing the action value of each agent and the influence on the actions of the other agent calculated for the selected action of each agent, and obtaining a sample to be used for training an action policy of each agent by probabilistically selecting the action in which the linear sum is the maximum, and the random action.
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公开(公告)号：US20200175355A1
公开(公告)日：2020-06-04
申请号：US16677835
申请日：2019-11-08
Inventor： Jaehoon CHUNG , Young-Su KWON , Chun-Gi LYUH , Chan KIM , Hyun Mi KIM , Jeongmin YANG , Yong Cheol Peter CHO
Abstract: A neural network accelerator in which processing elements are configured in a systolic array structure includes a memory to store a plurality of feature data including first and second feature data and a plurality of kernel data including first and second kernel data, a first processing element to perform an operation based on the first feature data and the first kernel data and output the first feature data, a selection circuit to select one of the first feature data and the second feature data, based on a control signal, and output the selected feature data, a second processing element to perform an operation based on the selected feature data and one of the first and the second kernel data, and a controller to generate the control signal, based on a neural network characteristic associated with the plurality of feature data and kernel data.
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公开(公告)号：US20240330649A1
公开(公告)日：2024-10-03
申请号：US18610804
申请日：2024-03-20
Inventor： Jeongmin YANG , Hyun Woo KIM , Hwajeon SONG , Byunghyun YOO , Euisok CHUNG , Ran HAN
IPC: G06N3/043
CPC classification number: G06N3/043
Abstract: Provided is an inference method employing a prompt-based meta-learning network and a computer system. The inference method includes selecting a task, generating a prompt key for the selected task using a prompt-embedding network (PEN), calculating similarities between the prompt key for the selected task and prompt keys included in a prompt key pool (PKP), acquiring a prompt value for the selected task using a memory network (MN), and generating an inference result for the selected task using a model-agnostic meta-learning (MAML)-based pre-trained model (MPM).
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公开(公告)号：US20180159699A1
公开(公告)日：2018-06-07
申请号：US15823104
申请日：2017-11-27
Inventor： Jeongmin YANG , Sung Weon KANG , Young-Su KWON
IPC: H04L12/40 , H04L12/863 , H04L12/413
CPC classification number: H04L12/40032 , G05B2219/25032 , H04L12/4135 , H04L47/6245 , H04L2012/40215
Abstract: Provided are Controller Area Network (CAN) controller and a data transmission method using the same. The CAN controller includes a receiver, a reception First in First out (FIFO) memory, a transmission FIFO memory, and a transmitter. The receiver is configured to analyze reception information received from a CAN bus according to a set protocol. The reception FIFO memory is configured to store the reception information to be overwritten on previously stored reception information based on identification data of the reception information and a bus load. The transmission FIFO memory is configured to store the transmission information to be overwritten on previously stored transmission information based on identification data of the transmission information and a processor load of the processor. The transmitter is configured to set the protocol and transmit the transmission information stored in the transmission FIFO memory to the CAN bus.
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5.发明公开公开(公告)号：US20240256885A1
公开(公告)日：2024-08-01
申请号：US18517931
申请日：2023-11-22
Inventor： Byunghyun YOO , Hyun Woo KIM , Hwajeon SONG , Jeongmin YANG , Sungwon YI , Euisok CHUNG , Ran HAN
IPC: G06N3/092
CPC classification number: G06N3/092
Abstract: Provided is an exploration method based on reward decomposition in multi-agent reinforcement learning. The exploration method includes: generating a positive reward estimation model through neural network training based on training data including states of all agents, actions of all the agents, and a global reward true value; generating, for each of the agents, a first individual utility function based on the global reward true value and generating a second individual utility function using the positive reward estimation model; and determining an action of each of the agents using the first individual utility function and the second individual utility function based on the state of each of the agents.
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Patent Agency Ranking
公开(公告)号：US20230274127A1
公开(公告)日：2023-08-31
申请号：US18088428
申请日：2022-12-23
Inventor： Hyun Woo KIM , Jeon Gue PARK , Hwajeon SONG , Jeongmin YANG , Byunghyun YOO , Euisok CHUNG , Ran HAN
IPC: G06N3/045 , G06F18/15 , G06F18/213 , G06F18/22
CPC classification number: G06N3/045 , G06F18/15 , G06F18/213 , G06F18/22
Abstract: A concept based few-shot learning method is disclosed. The method includes estimating a task embedding corresponding to a task to be executed from support data that is a small amount of learning data; calculating a slot probability of a concept memory necessary for a task based on the task embedding; extracting features of query data that is test data, and of the support data; comparing local features for the extracted features with slots of a concept memory to extract a concept, and generating synthesis features to have maximum similarity to the extracted features through the slots of the concept memory; and calculating a task execution result from the synthesis feature and the extracted concept by applying the slot probability as a weight.
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公开(公告)号：US20190220739A1
公开(公告)日：2019-07-18
申请号：US16225729
申请日：2018-12-19
Inventor： Young-Su KWON , Hyun Mi KIM , Jeongmin YANG
Abstract: Provided is a neural network computing device including a neural network memory configured to store input data, a kernel memory configured to store kernel data corresponding to the input data, a kernel data controller configured to determine whether or not a first part of the kernel data matches a predetermined bit string, and if the first part matches the predetermined bit string, configured to generate a plurality of specific data based on a second part of the kernel data, and a neural core configured to perform a first operation between one of the plurality of specific data and the input data.
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8.发明公开公开(公告)号：US20240202454A1
公开(公告)日：2024-06-20
申请号：US18471538
申请日：2023-09-21
Inventor： Euisok CHUNG , Hyun Woo KIM , Hwajeon SONG , Jeongmin YANG , Byunghyun YOO , Ran HAN
IPC: G06F40/30 , G06F40/284
CPC classification number: G06F40/30 , G06F40/284
Abstract: A domain adaptation procedure, such as fine-tuning training, is required to utilize a large-capacity PLM for a specific domain. Attempts in existing research have been made to improve performance of a PLM through domain adaptor technology based on an N-gram in order to reduce errors on the basis of the results of domain text error analysis of the PLM. Proposed is a method of selecting a semantic chunk through a domain semantic chunk graph and PageRank based on the existing domain adaptor research, with an N-gram as the semantic chunk. Proposed is also a method of domain-adapting a large-capacity PLM using semantic chunk dynamic weight masking, which reflects an output value of a PLM rather than simply integrating embedding values of semantic chunks, in a semantic chunk domain adaptor technology.
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公开(公告)号：US20220147353A1
公开(公告)日：2022-05-12
申请号：US17446678
申请日：2021-09-01
Inventor： Jeongmin YANG
IPC: G06F9/30 , G06F9/38 , G06F17/18 , G06F12/0875
Abstract: Disclosed is a general-purpose computing accelerator which includes a memory including an instruction cache, a first executing unit performing a first computation operation, a second executing unit performing a second computation operation, an instruction fetching unit fetching an instruction stored in the instruction cache, a decoding unit that decodes the instruction, and a state control unit controlling a path of the instruction depending on an operation state of the second executing unit. The decoding unit provides the instruction to the first executing unit when the instruction is of a first type and provides the instruction to the state control unit when the instruction is of a second type. Depending on the operation state of the second executing unit, the state control unit provides the instruction of the second type to the second executing unit or stores the instruction of the second type as a register file in the memory.
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公开(公告)号：US20190079801A1
公开(公告)日：2019-03-14
申请号：US16038243
申请日：2018-07-18
Inventor： Chun-Gi LYUH , Young-Su KWON , Chan KIM , Hyun Mi KIM , Jeongmin YANG , Jaehoon CHUNG , Yong Cheol Peter CHO
Abstract: Provided is a neural network accelerator which performs a calculation of a neural network provided with layers, the neural network accelerator including a kernel memory configured to store kernel data related to a filter, a feature map memory configured to store feature map data which are outputs of the layers, and a Processing Element (PE) array including PEs arranged along first and second directions, wherein each of the PEs performs a calculation using the feature map data transmitted in the first direction from the feature map memory and the kernel data transmitted in the second direction from the kernel memory, and transmits a calculation result to the feature map memory in a third direction opposite to the first direction.
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