公开(公告)号：US12237846B2

公开(公告)日：2025-02-25

申请号：US18158332

申请日：2023-01-23

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Fa-Long Luo ,  Jaime Cummins

IPC: H03M13/37 ,  G06F7/544 ,  G06N3/08

Abstract: Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate an error-reduced version 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 and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate an error-reduced version of encoded data for an error correction coding (ECC) decoder, e.g., to facilitate decoding of the error-reduced version of encoded data at the decoder. In this manner, neural networks or recurrent neural networks described herein may be used to improve or facilitate aspects of decoding at ECC decoders, e.g., by reducing errors present in encoded data due to storage or transmission.

公开(公告)号：US20250047320A1

公开(公告)日：2025-02-06

申请号：US18583689

申请日：2024-02-21

Applicant: Micron Technology, Inc.

Inventor： Fa-Long Luo ,  Jaime Cummins

IPC: H04B1/401 ,  H04B7/06 ,  H04B7/08

Abstract: A system includes a first wireless communication device comprising a first baseband processor neural network configured to process at least part of data for transmission to a second wireless communication device according to a collaborative processing configuration while collaborative processing is enabled to generate a first radio frequency (RF) signal. The first wireless communication device is configured to transmit the first RF signal. The system further includes a third wireless communication device comprising a second baseband processor neural network configured to, while the collaborative processing is enabled, process at least part of the data for transmission to the second wireless communication device according to a collaborative processing configuration to generate a second RF signal. The third wireless communication device is configured to transmit the second RF signal in collaboration with transmission of the first RF signal by the first baseband processor.

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：US20240369632A1

公开(公告)日：2024-11-07

申请号：US18772690

申请日：2024-07-15

Applicant: Micron Technology, Inc.

Inventor： Kenneth M. Curewitz ,  Jaime Cummins ,  John D. Porter ,  Bryce D. Cook ,  Jeffrey P. Wright

IPC: G01R31/319 ,  G01R31/3185

Abstract: A memory controller and a physical interface layer may accommodate multiple memory types. In some examples, the memory controller and/or PHY may include a register that includes operating parameters for multiple operating modes. Different operating modes may be compatible with different memory types. In some examples, the memory controller and physical interface may be included in a system for testing multiple memory types. The system may provide multiple interfaces for communicating with the memory. The different communication types may be used for performing different tests and/or simulating different types of devices that may utilize the memory.

公开(公告)号：US12072381B2

公开(公告)日：2024-08-27

申请号：US18047386

申请日：2022-10-18

Applicant: MICRON TECHNOLOGY, INC.

Inventor： Kenneth M. Curewitz ,  Jaime Cummins ,  John D. Porter ,  Bryce D. Cook ,  Jeffrey P. Wright

IPC: G01R31/319 ,  G01R31/3185

CPC classification number: G01R31/31907 ,  G01R31/318594 ,  G01R31/318597

Abstract: A memory controller and a physical interface layer may accommodate multiple memory types. In some examples, the memory controller and/or PHY may include a register that includes operating parameters for multiple operating modes. Different operating modes may be compatible with different memory types. In some examples, the memory controller and physical interface may be included in a system for testing multiple memory types. The system may provide multiple interfaces for communicating with the memory. The different communication types may be used for performing different tests and/or simulating different types of devices that may utilize the memory.

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：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.