Error correction for content-addressable memory

    公开(公告)号:US20210064455A1

    公开(公告)日:2021-03-04

    申请号:US16553731

    申请日:2019-08-28

    Abstract: Methods, systems, and devices for error correction for content-addressable memory (CAM) are described. A CAM may store bit vectors as a set of subvectors, which each subvector stored in an independent aspect of the CAM, such as in a separate column or array of memory cells within the CAM. The CAM may similarly segment a queried input bit vector and identify, for each resulting input subvector, whether a matching subvector is stored by the CAM. The CAM may identify a match for the input bit vector when the number of matching subvectors satisfies a threshold. The CAM may validate a match based on comparing a stored bit vector corresponding to the identified match to the input bit vector. The stored bit vector may undergo error correction and may be stored in the CAM or another memory array, such as a dynamic random access memory (DRAM) array.

    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.

    Memory Management Unit (MMU) for Accessing Borrowed Memory

    公开(公告)号:US20200379919A1

    公开(公告)日:2020-12-03

    申请号:US16424420

    申请日:2019-05-28

    Abstract: Systems, methods and apparatuses to accelerate accessing of borrowed memory over network connection are described. For example, a memory management unit (MMU) of a computing device can be configured to be connected both to the random access memory over a memory bus and to a computer network via a communication device. The computing device can borrow an amount of memory from a remote device over a network connection using the communication device; and applications running in the computing device can use virtual memory addresses mapped to the borrowed memory. When a virtual address mapped to the borrowed memory is used, the MMU translates the virtual address into a physical address and instruct the communication device to access the borrowed memory.

    MEMORY DEVICES WITH SELECTIVE PAGE-BASED REFRESH

    公开(公告)号:US20200342933A1

    公开(公告)日:2020-10-29

    申请号:US16926582

    申请日:2020-07-10

    Inventor: Ameen D. Akel

    Abstract: Several embodiments of memory devices and systems with selective page-based refresh are disclosed herein. In one embodiment, a memory device includes a controller operably coupled to a main memory having at least one memory region comprising a plurality of memory pages. The controller is configured to track, in one or more refresh schedule tables stored on the memory device and/or on a host device, a subset of memory pages in the plurality of memory pages configured to be refreshed according to a refresh schedule. In some embodiments, the controller is further configured to refresh the subset of memory pages in accordance with the refresh schedule.

    Memory devices with selective page-based refresh

    公开(公告)号:US10109339B1

    公开(公告)日:2018-10-23

    申请号:US15663641

    申请日:2017-07-28

    Inventor: Ameen D. Akel

    Abstract: Several embodiments of memory devices and systems with selective page-based refresh are disclosed herein. In one embodiment, a memory device includes a controller operably coupled to a main memory having at least one memory region comprising a plurality of memory pages. The controller is configured to track, in one or more refresh schedule tables stored on the memory device and/or on a host device, a subset of memory pages in the plurality of memory pages having an refresh schedule. In some embodiments, the controller is further configured to refresh the subset of memory pages in accordance with the refresh schedule.

    Redundant computing across planes
    28.
    发明授权

    公开(公告)号:US12282682B2

    公开(公告)日:2025-04-22

    申请号:US18415285

    申请日:2024-01-17

    Abstract: Methods, systems, and devices for redundant computing across planes are described. A device may perform a computational operation on first data that is stored in a first plane that includes content-addressable memory cells. The first data may be representative of a set of contiguous bits of a vector. The device may perform, concurrent with performing the computational operation on the first data, the computational operation on second data that is stored in a second plane. The second data may be representative of the set of contiguous bits of the vector. The device may read from the first plane and write to the second plane, third data representative of a result of the computational operation on the first data.

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