公开(公告)号：US12217164B2

公开(公告)日：2025-02-04

申请号：US16964435

申请日：2018-02-24

Applicant: Tsinghua University

Inventor： Xinyi Li ,  Huaqiang Wu ,  He Qian ,  Bin Gao ,  Sen Song ,  Qingtian Zhang

IPC: G06N3/06 ,  G06N3/049 ,  G06N3/065 ,  G06N3/084

Abstract: A neural network and its information processing method, information processing system. The neural network includes N layers of neuron layers connected to each other one by one, except for a first layer of neuron layer, each of the neurons of the other neuron layers includes m dendritic units and one hippocampal unit; the dendritic unit includes a resistance value graded device, the hippocampal unit includes a resistance value mutation device, and the m dendritic units can be provided with different threshold voltage or current, respectively; and the neurons on the nth layer neuron layer are connected to the m dendritic units of the neurons on the n+1th layer neuron layer; wherein N is an integer larger than 3, m is an integer larger than 1, n is an integer larger than 1 and less than N.

公开(公告)号：US20240385927A1

公开(公告)日：2024-11-21

申请号：US18665946

申请日：2024-05-16

Applicant: Micron Technology, Inc.

Inventor： Fa-Long Luo ,  Jaime Cummins

IPC: G06F11/10 ,  G06N3/06

Abstract: Apparatuses and methods for error correction based on data characteristics are disclosed. Data characteristics can include importance of the data. Data is received at a memory controller from a host device, and a characteristic of the received data is determined. A level of error correction is selected from a plurality of error correction levels for the received data based on the determined characteristic. The received data and an error correction code are written to a memory. The error correction code is generated based on the selected level of error correction. In some implementations, the characteristic of the received data is determined using a neural network.

公开(公告)号：US12147889B2

公开(公告)日：2024-11-19

申请号：US16373745

申请日：2019-04-03

Applicant: Realtek Semiconductor Corp.

Inventor： Chia-Liang (Leon) Lin ,  Shih-Chun Wei

IPC: G06N3/063 ,  G06N3/04 ,  G06N3/06 ,  H03K17/16

Abstract: An artificial neural network and method are provided. The method includes receiving a set of input voltages; converting a respective input voltage in said set of input voltages into a respective set of local currents using a voltage-to-current conversion; multiplying said respective set of local currents by a respective set of binary signals to establish a respective set of conditionally inverted currents; summing said respective set of conditionally inverted currents into a respective local current; summing all respective local currents into a global current; and converting the global current into an output voltage using a load circuit.

公开(公告)号：US20240324942A1

公开(公告)日：2024-10-03

申请号：US18742949

申请日：2024-06-13

Applicant: NeuroGeneces Inc.

Inventor： Marko Šarlija ,  Michael Kenneth Comerford, III ,  Karen Crow

IPC: A61B5/377 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/024 ,  A61B5/291 ,  A61B5/31 ,  A61B5/369 ,  A61B5/374 ,  A61B5/384 ,  G06F3/01 ,  G06N3/02 ,  G06N3/06 ,  G06N3/08 ,  G16H50/70

CPC classification number: A61B5/377 ,  A61B5/0205 ,  A61B5/4812 ,  A61B5/725 ,  A61B5/7267 ,  A61B5/7275 ,  A61B5/02416 ,  A61B5/291 ,  A61B5/31 ,  A61B5/369 ,  A61B5/374 ,  A61B5/384 ,  A61B5/4806 ,  A61B5/4809 ,  A61B5/4815 ,  A61B5/72 ,  A61B5/7235 ,  G06F3/015 ,  G06N3/02 ,  G06N3/06 ,  G06N3/08 ,  G16H50/70

Abstract: Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

公开(公告)号：US20240321999A1

公开(公告)日：2024-09-26

申请号：US18024277

申请日：2022-07-08

Applicant: Fudan University

Inventor： Jianlu WANG ,  Guangjian WU

IPC: H01L29/51 ,  G06N3/06 ,  H01L31/113 ,  H01L31/18

CPC classification number: H01L29/516 ,  G06N3/06 ,  H01L31/1136 ,  H01L31/18

Abstract: The present invention relates to a photo-response detector, in particular to a ferroelectric field modulated positive and negative photo-response detector, a preparation method and application thereof. The ferroelectric field modulated positive and negative photo-response detector includes a substrate, a gate electrode, a ferroelectric layer, a low-dimensional semiconductor and a source-drain electrode. A pair of gate electrodes are provided and fixedly arranged on the substrate at intervals. The ferroelectric layer is fixedly arranged on the substrate and completely covers the gate electrode. The low-dimensional semiconductor is fixedly arranged on the ferroelectric layer. The source-drain electrode includes a source electrode and a drain electrode separately arranged on two sides of the low-dimensional semiconductor and fixedly arranged on the ferroelectric layer.

公开(公告)号：US12086703B2

公开(公告)日：2024-09-10

申请号：US17445377

申请日：2021-08-18

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Bambi L DeLaRosa ,  Katya Giannios ,  Abhishek Chaurasia

IPC: G06T5/70 ,  G06N3/048 ,  G06N3/06

CPC classification number: G06N3/048 ,  G06N3/06 ,  G06T5/70

Abstract: In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

公开(公告)号：US12061977B2

公开(公告)日：2024-08-13

申请号：US17675617

申请日：2022-02-18

Applicant: Analog Devices, Inc.

Inventor： Eric G. Nestler ,  Naveen Verma ,  Hossein Valavi

IPC: G06N3/065 ,  G06N3/045 ,  G06N3/048 ,  G06N3/06 ,  G11C11/54

CPC classification number: G06N3/065 ,  G06N3/045 ,  G06N3/048 ,  G06N3/061 ,  G11C11/54

Abstract: Systems and methods are provided for reducing power in in-memory computing, matrix-vector computations, and neural networks. An apparatus for in-memory computing using charge-domain circuit operation includes transistors configured as memory bit cells, transistors configured to perform in-memory computing using the memory bit cells, capacitors configured to store a result of in-memory computing from the memory bit cells, and switches, wherein, based on a setting of each of the switches, the charges on at least a portion of the plurality of capacitors are shorted together. Shorting together the plurality of capacitors yields a computation result.

公开(公告)号：US12053288B2

公开(公告)日：2024-08-06

申请号：US18137250

申请日：2023-04-20

Applicant: NeuroGeneces Inc.

Inventor： Marko {hacek over (S)}arlija ,  Michael Kenneth Comerford, III ,  Karen Crow

IPC: A61B5/377 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/024 ,  A61B5/291 ,  A61B5/31 ,  A61B5/369 ,  A61B5/374 ,  A61B5/384 ,  G06F3/01 ,  G06N3/02 ,  G06N3/06 ,  G06N3/08 ,  G16H50/70

CPC classification number: A61B5/377 ,  A61B5/0205 ,  A61B5/4812 ,  A61B5/725 ,  A61B5/7267 ,  A61B5/7275 ,  A61B5/02416 ,  A61B5/291 ,  A61B5/31 ,  A61B5/369 ,  A61B5/374 ,  A61B5/384 ,  A61B5/4806 ,  A61B5/4809 ,  A61B5/4815 ,  A61B5/72 ,  A61B5/7235 ,  G06F3/015 ,  G06N3/02 ,  G06N3/06 ,  G06N3/08 ,  G16H50/70

Abstract: Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

公开(公告)号：US12051013B2

公开(公告)日：2024-07-30

申请号：US16968606

申请日：2019-03-05

Applicant: OMRON Corporation

Inventor： Takashi Fujii ,  Yuki Ueyama ,  Yasuaki Abe ,  Nobuyuki Sakatani ,  Kazuhiko Imatake

IPC: G06N7/04 ,  B25J9/16 ,  B25J15/02 ,  G05B13/02 ,  G06N3/06 ,  G06N3/08 ,  G06N20/00

CPC classification number: G06N7/046 ,  B25J9/161 ,  B25J9/163 ,  B25J15/02 ,  G05B13/0265 ,  G06N3/06 ,  G06N3/08 ,  G06N7/04 ,  G06N20/00 ,  G05B2219/39284

Abstract: This learning device provides a learned model to an adjuster including the learned model learned to output a predetermined compensation amount to a controller based on parameters of an object to be processed, in a system including the controller outputting a command value obtained by compensating a target value based on a compensation amount; and a control object performing a predetermined process on the object and outputting a control variable as a response to the command value. The learning device includes: a learning part generating candidate compensation amounts based on operation data including a target value, command value and control variable, learning with the generated candidate compensation amounts and the parameters of the object as teacher data, and generating or updating the learned model; and a setting part providing, to the adjuster, the generated or updated learned model.

公开(公告)号：US11989597B2

公开(公告)日：2024-05-21

申请号：US17505840

申请日：2021-10-20

Applicant: CAPITAL ONE SERVICES, LLC

Inventor： Austin Walters ,  Mark Watson ,  Galen Rafferty ,  Anh Truong ,  Jeremy Goodsitt ,  Vincent Pham

IPC: G06F16/00 ,  G06F8/71 ,  G06F9/54 ,  G06F11/36 ,  G06F16/22 ,  G06F16/242 ,  G06F16/2455 ,  G06F16/248 ,  G06F16/25 ,  G06F16/28 ,  G06F16/335 ,  G06F16/903 ,  G06F16/9032 ,  G06F16/9038 ,  G06F16/906 ,  G06F16/93 ,  G06F17/15 ,  G06F17/16 ,  G06F17/18 ,  G06F18/20 ,  G06F18/21 ,  G06F18/2115 ,  G06F18/213 ,  G06F18/214 ,  G06F18/22 ,  G06F18/23 ,  G06F18/24 ,  G06F18/2411 ,  G06F18/2415 ,  G06F18/40 ,  G06F21/55 ,  G06F21/60 ,  G06F21/62 ,  G06F30/20 ,  G06F40/117 ,  G06F40/166 ,  G06F40/20 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/06 ,  G06N3/08 ,  G06N3/088 ,  G06N5/00 ,  G06N5/02 ,  G06N5/04 ,  G06N7/00 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/04 ,  G06T7/194 ,  G06T7/246 ,  G06T7/254 ,  G06T11/00 ,  G06V10/70 ,  G06V10/98 ,  G06V30/194 ,  G06V30/196 ,  H04L9/40 ,  H04L67/00 ,  H04L67/306 ,  H04N21/234 ,  H04N21/81

CPC classification number: G06F9/541 ,  G06F8/71 ,  G06F9/54 ,  G06F9/547 ,  G06F11/3608 ,  G06F11/3628 ,  G06F11/3636 ,  G06F16/2237 ,  G06F16/2264 ,  G06F16/2423 ,  G06F16/24568 ,  G06F16/248 ,  G06F16/254 ,  G06F16/258 ,  G06F16/283 ,  G06F16/285 ,  G06F16/288 ,  G06F16/335 ,  G06F16/90332 ,  G06F16/90335 ,  G06F16/9038 ,  G06F16/906 ,  G06F16/93 ,  G06F17/15 ,  G06F17/16 ,  G06F17/18 ,  G06F18/2115 ,  G06F18/213 ,  G06F18/214 ,  G06F18/2148 ,  G06F18/217 ,  G06F18/2193 ,  G06F18/22 ,  G06F18/23 ,  G06F18/24 ,  G06F18/2411 ,  G06F18/2415 ,  G06F18/285 ,  G06F18/40 ,  G06F21/552 ,  G06F21/60 ,  G06F21/6245 ,  G06F21/6254 ,  G06F30/20 ,  G06F40/117 ,  G06F40/166 ,  G06F40/20 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/06 ,  G06N3/08 ,  G06N3/088 ,  G06N5/00 ,  G06N5/02 ,  G06N5/04 ,  G06N7/00 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/04 ,  G06T7/194 ,  G06T7/246 ,  G06T7/248 ,  G06T7/254 ,  G06T11/001 ,  G06V10/768 ,  G06V10/993 ,  G06V30/194 ,  G06V30/1985 ,  H04L63/1416 ,  H04L63/1491 ,  H04L67/306 ,  H04L67/34 ,  H04N21/23412 ,  H04N21/8153 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for connecting datasets are disclosed. For example, a system may include a memory unit storing instructions and a processor configured to execute the instructions to perform operations. The operations may include receiving a plurality of datasets and a request to identify a cluster of connected datasets among the received plurality of datasets. The operations may include selecting a dataset. In some embodiments, the operations include identifying a data schema of the selected dataset and determining a statistical metric of the selected dataset. The operations may include identifying foreign key scores. The operations may include generating a plurality of edges between the datasets based on the foreign key scores, the data schema, and the statistical metric. The operations may include segmenting and returning datasets based on the plurality of edges.