MACHINE LEARNING WITH FEATURE OBFUSCATION

    公开(公告)号:US20210056405A1

    公开(公告)日:2021-02-25

    申请号:US16545837

    申请日:2019-08-20

    Abstract: A system having multiple devices that can host different versions of an artificial neural network (ANN). In the system, inputs for the ANN can be obfuscated for centralized training of a master version of the ANN at a first computing device. A second computing device in the system includes memory that stores a local version of the ANN and user data for inputting into the local version. The second computing device includes a processor that extracts features from the user data and obfuscates the extracted features to generate obfuscated user data. The second device includes a transceiver that transmits the obfuscated user data. The first computing device includes a memory that stores the master version of the ANN, a transceiver that receives obfuscated user data transmitted from the second computing device, and a processor that trains the master version based on the received obfuscated user data using machine learning.

    DISTRIBUTED MACHINE LEARNING WITH PRIVACY PROTECTION

    公开(公告)号:US20210056387A1

    公开(公告)日:2021-02-25

    申请号:US16545813

    申请日:2019-08-20

    Abstract: A system having multiple devices that can host different versions of an artificial neural network (ANN). In the system, changes to local versions of the ANN can be combined with a master version of the ANN. In the system, a first device can include memory that can store the master version, a second device can include memory that can store a local version of the ANN, and there can be many devices that store local versions of the ANN. The second device (or any other device of the system hosting a local version) can include a processor that can train the local version, and a transceiver that can transmit changes to the local version generated from the training. The first device can include a transceiver that can receive the changes to a local version, and a processing device that can combine the received changes with the master version.

    Page table hooks to memory types
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    发明授权

    公开(公告)号:US11494311B2

    公开(公告)日:2022-11-08

    申请号:US16573527

    申请日:2019-09-17

    Abstract: A computer system includes physical memory devices of different types that store randomly-accessible data in memory of the computer system. In one approach, access to memory in an address space is maintained by an operating system of the computer system. A virtual page is associated with a first memory type. A page table entry is generated to map a virtual address of the virtual page to a physical address in a first memory device of the first memory type. The page table entry is used by a memory management unit to store the virtual page at the physical address.

    DATA PROTECTION TECHNIQUES IN STACKED MEMORY ARCHITECTURES

    公开(公告)号:US20250077353A1

    公开(公告)日:2025-03-06

    申请号:US18762284

    申请日:2024-07-02

    Abstract: Methods, systems, and devices for data protection techniques in stacked memory architectures are described. A memory system having a stacked memory architecture may include error correction information associated with a data set that includes multiple data segments stored across multiple memory arrays and, in some examples, multiple dies of the memory system. As part of a write operation for a first data segment of a data set, the memory system may retrieve the remaining data segments of the data set and calculate error correction information using the first data segment and the remaining data segments. As part of a read operation for a second data segment of the data set, the memory system may retrieve each data segment of the data set and perform an error correction operation on the data set using the error correction information.

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