RESOURCE EFFICIENT FEDERATED EDGE LEARNING WITH HYPERDIMENSIONAL COMPUTING

    公开(公告)号:US20240054403A1

    公开(公告)日:2024-02-15

    申请号:US18384525

    申请日:2023-10-27

    申请人: Intel Corporation

    IPC分类号: G06N20/00

    CPC分类号: G06N20/00

    摘要: A device to train a hyperdimensional computing (HDC) model may include memory and processing circuitry to train one or more independent sub models of the HDC model and transmit the one or more independent sub models to another computing device, such as a server. The device may be one of a plurality of devices, such as edge computing devices, edge or Internet of Things (IoT) nodes, or the like. Training of the one or more independent sub models of the HDC model may include transforming one or more training data points to one or more hyperdimensional representations, initializing a prototype using the hyperdimensional representations of the one or more training data points, and iteratively training the initialized prototype.

    QUEUING CONTROL FOR DISTRIBUTED COMPUTE NETWORK ORCHESTRATION

    公开(公告)号:US20220224762A1

    公开(公告)日:2022-07-14

    申请号:US17712119

    申请日:2022-04-02

    申请人: Intel Corporation

    IPC分类号: H04L67/51 H04L67/63

    摘要: In one embodiment, a node of a data centric network (DCN) may receive a first service request interest packet from another node of the DCN, the first service request interest packet indicating a set of functions to be performed on source data to implement a service. The node may determine that it can perform a particular function of the set of functions, and determine, based on a backlog information corresponding to the particular function, whether to commit to performing the particular function or to forward the service request interest packet to another node. The node may make the determination further based on service delivery information indicating, for each face of the node, a service delivery distance for implementing the set of functions.