公开(公告)号：US20240411471A1

公开(公告)日：2024-12-12

申请号：US18734270

申请日：2024-06-05

Applicant: Micron Technology, Inc.

Inventor： Fa-Long Luo ,  Jaime Cummins

IPC: G06F3/06

Abstract: The present invention relates to a memory controller and a memory device that are configured to communicate with each other using multiple input multiple output (MIMO) technology. The memory controller includes a precoder that precodes data for transmission. The precoding is based on channel state information, a neural network, or both. The memory device receives the precoded data and decodes them to retrieve the original data. In some cases, the precoder uses the channel state information to optimize the precoding matrix for the given channel conditions. In some cases, a neural network is trained to predict the optimal precoding matrix for the current channel state. The precoding matrix is then used to encode the data, which is then transmitted to the memory device. The use of MIMO and precoding improves the reliability and efficiency of the communication between the memory controller and memory device.

公开(公告)号：US20240379181A1

公开(公告)日：2024-11-14

申请号：US18650444

申请日：2024-04-30

Applicant: Micron Technology, Inc.

Inventor： Fa-Long Luo ,  Jaime Cummins

IPC: G11C29/12 ,  G11C11/54 ,  G11C29/36

Abstract: A memory includes a read/write amplifier configured to retrieve read data from a memory array, and a neural network based preconditioning circuit configured to receive a read data signal according to the read data. A neural network of the preconditioning circuit is configured to precondition the read data signal based on a characteristic of a read data transmission path to provide a modified read data signal. The memory further includes an output driver configured to transmit the modified read data signal.

公开(公告)号：US11973525B2

公开(公告)日：2024-04-30

申请号：US18065062

申请日：2022-12-13

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Fa-Long Luo ,  Jaime Cummins ,  Tamara Schmitz ,  Jeremy Chritz

IPC: H04B1/04 ,  H04B1/525 ,  H04L5/14

CPC classification number: H04B1/0475 ,  H04B1/525 ,  H04L5/14

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of full duplex compensation with a self-interference noise calculator that compensates for the self-interference noise generated by power amplifiers at harmonic frequencies of a respective wireless receiver. The self-interference noise calculator may be coupled to antennas of a wireless device and configured to generate the adjusted signals that compensate self-interference. The self-interference noise calculator may include a network of processing elements configured to combine transmission signals into sets of intermediate results. Each set of intermediate results may be summed in the self-interference noise calculator to generate a corresponding adjusted signal. The adjusted signal is receivable by a corresponding wireless receiver to compensate for the self-interference noise generated by a wireless transmitter transmitting on the same or different frequency band as the wireless receiver is receiving.

公开(公告)号：US11941518B2

公开(公告)日：2024-03-26

申请号：US16114923

申请日：2018-08-28

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Fa-Long Luo ,  Tamara Schmitz ,  Jeremy Chritz ,  Jaime Cummins

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/045 ,  H04W4/02 ,  H04W4/38 ,  H04W4/40 ,  H04W4/90

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/045 ,  H04W4/02 ,  H04W4/023 ,  H04W4/38 ,  H04W4/40 ,  H04W4/90

Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.

公开(公告)号：US11941516B2

公开(公告)日：2024-03-26

申请号：US15693142

申请日：2017-08-31

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Fa-Long Luo ,  Tamara Schmitz ,  Jeremy Chritz ,  Jaime Cummins

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/045 ,  H04W4/02 ,  H04W4/38 ,  H04W4/40 ,  H04W4/90

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/045 ,  H04W4/02 ,  H04W4/023 ,  H04W4/38 ,  H04W4/40 ,  H04W4/90

Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.

公开(公告)号：US20240078286A1

公开(公告)日：2024-03-07

申请号：US18502435

申请日：2023-11-06

Applicant: Micron Technology, Inc.

Inventor： Fa-Long Luo

IPC: G06F17/16 ,  G06F9/30 ,  G06F12/02 ,  G11C13/00

CPC classification number: G06F17/16 ,  G06F9/3001 ,  G06F9/30036 ,  G06F12/0207 ,  G11C13/004 ,  G11C13/0069 ,  G06F2212/454

Abstract: Methods and apparatus for performing diversity matrix operations within a memory fabric. Various embodiments of the present disclosure are directed to converting a memory array into a matrix fabric for spatial diversity-related matrix transformations and performing matrix operations therein. Exemplary embodiments described herein perform MIMO-related matrix transformations (e.g., precoding, beamforming, or data recovery matrix operations) within a memory device that includes a matrix fabric and matrix multiplication unit (MMU). In one variant, the matrix fabric uses a “crossbar” construction of resistive elements. Each resistive element stores a level of impedance that represents the corresponding matrix coefficient value. The crossbar connectivity can be driven with an electrical signal representing the input vector as an analog voltage. The resulting signals can be converted from analog voltages to a digital values by an MMU to yield a matrix-vector product. The MMU may additionally perform various other logical operations within the digital domain.

公开(公告)号：US20230308518A1

公开(公告)日：2023-09-28

申请号：US18324853

申请日：2023-05-26

Applicant: Micron Technology, Inc.

Inventor： Richard Cameron Murphy ,  Fa-Long Luo

IPC: H04L67/51 ,  H04L67/1097 ,  H04W4/24 ,  G06F16/22 ,  H04L9/06 ,  G06Q40/12 ,  H04L12/14 ,  H04L67/50

CPC classification number: H04L67/51 ,  H04L67/1097 ,  H04W4/24 ,  G06F16/2272 ,  H04L9/0637 ,  G06Q40/12 ,  H04L12/1428 ,  H04L67/535 ,  H04L41/5096

Abstract: Methods and apparatus for incentivizing device participation within a distributed network. In one exemplary embodiment, devices of a fog network may provide for example, computational, storage, and/or network resources in exchange for fungible tokens. In one such variant, the user contributions are recorded in a blockchain data structure, thereby enabling users to be compensated for their contributions of resources to the network at a later time. Unlike traditional networking techniques which often rely on centralized networks directing and/or commandeering user equipment for network bandwidth, the various aspects of the present disclosure are directed to ensuring that crediting and debiting of participation can be performed at the edge of the network (within the fog) without requiring authentication or trust exchanges. More directly, various aspects of the present disclosure are directed to verification and/or validation of work performed by peer devices.

公开(公告)号：US11755408B2

公开(公告)日：2023-09-12

申请号：US17496703

申请日：2021-10-07

Applicant: Micron Technology, Inc.

Inventor： Fa-Long Luo ,  Jaime Cummins

IPC: H03M13/27 ,  H04L27/34 ,  H04L1/00 ,  H03M13/25 ,  G06F11/10 ,  G06N3/045

CPC classification number: G06F11/1068 ,  G06N3/045

Abstract: Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks, to estimate a bit error rate (BER) of encoded data based on a retrieved version of encoded data (e.g., data encoded using one or more encoding techniques) from a memory. The neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous to estimate a BER of encoded data, e.g., to facilitate decoding of the encoded data. In this manner, neural networks described herein may be used to improve or facilitate aspects of decoding at ECC decoders, e.g., by comparing an estimated BER to a threshold (e.g., a threshold BER level) prior to decoding of the encoded data. For example, an additional NN activation indication may be provided, e.g., to indicate that the encoded data may be decoded or to indicate that error present in the encoded data is to be reduced.

公开(公告)号：US20230237383A1

公开(公告)日：2023-07-27

申请号：US18194536

申请日：2023-03-31

Applicant: Micron Technology, Inc.

Inventor： Fa-Long Luo

IPC: G06N20/00 ,  G06V10/143 ,  G06V40/16 ,  G06F18/214 ,  G06V10/776

CPC classification number: G06N20/00 ,  G06V10/143 ,  G06V40/166 ,  G06F18/2148 ,  G06V10/776

Abstract: Methods and apparatus for applying data analytics such as deep learning algorithms to sensor data. In one embodiment, an electronic device such as a camera apparatus including a deep learning accelerator (DLA) communicative with an image sensor is disclosed, the camera apparatus configured to evaluate unprocessed sensor data from the image sensor using the DLA. In one variant, the camera apparatus provides sensor data directly to the DLA, bypassing image signal processing in order to improve the effectiveness the DLA, obtain DLA results more quickly than using conventional methods, and further allow the camera apparatus to conserve power.

公开(公告)号：US11710524B2

公开(公告)日：2023-07-25

申请号：US17398914

申请日：2021-08-10

Applicant: Micron Technology, Inc.

Inventor： Glen E. Hush ,  Aaron P. Boehm ,  Fa-Long Luo

IPC: G11C7/00 ,  G11C16/10 ,  G11C7/10 ,  G11C16/08 ,  G06F17/16 ,  G11C8/14 ,  G11C5/02 ,  G11C16/04

CPC classification number: G11C16/10 ,  G06F17/16 ,  G11C5/02 ,  G11C7/1039 ,  G11C8/14 ,  G11C16/0483 ,  G11C16/08

Abstract: Systems, apparatuses, and methods related to organizing data to correspond to a matrix at a memory device are described. Data can be organized by circuitry coupled to an array of memory cells prior to the processing resources executing instructions on the data. The organization of data may thus occur on a memory device, rather than at an external processor. A controller coupled to the array of memory cells may direct the circuitry to organize the data in a matrix configuration to prepare the data for processing by the processing resources. The circuitry may be or include a column decode circuitry that organizes the data based on a command from the host associated with the processing resource. For example, data read in a prefetch operation may be selected to correspond to rows or columns of a matrix configuration.