公开(公告)号：US20250094823A1

公开(公告)日：2025-03-20

申请号：US18368801

申请日：2023-09-15

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Jayanth SRINIVASA ,  Ali PAYANI ,  Ramana Rao V.R. KOMPELLA

IPC: G06N3/098

Abstract: In one implementation, a controller determines performance of a partitioned neural network. The controller identifies, based on the performance, a particular partition of the partitioned neural network as a bottleneck. The controller configures a first device to execute a replica of the particular partition. The controller configures a multiplexer that provides an output of the particular partition or the replica of the particular partition as input to a downstream partition of the partitioned neural network.

公开(公告)号：US12008486B2

公开(公告)日：2024-06-11

申请号：US17173380

申请日：2021-02-11

Applicant: Cisco Technology, Inc.

Inventor： Hugo Latapie ,  Ozkan Kilic ,  Ramana Rao V. R. Kompella ,  Myungjin Lee ,  Simon Matthew Young

IPC: G06N5/043 ,  G06N3/042 ,  G06N3/088 ,  G06N5/02

CPC classification number: G06N5/043 ,  G06N3/042 ,  G06N5/02 ,  G06N3/088

Abstract: In one embodiment, a device maintains a metamodel that describes a monitored system. The metamodel comprises a plurality of layers ranging from a sub-symbolic space to a symbolic space. The device tracks updates to the metamodel over time. The device updates the metamodel based in part on sub-symbolic time series data generated by the monitored system. The device receives, from a learning agent, a request for the updates to a particular layer of the metamodel associated with a specified time period. The device provides, to the learning agent, data indicative of one or more updates to the particular layer of the metamodel associated with the specified time period.

公开(公告)号：US20230385708A1

公开(公告)日：2023-11-30

申请号：US17828582

申请日：2022-05-31

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Gaoxiang Luo ,  Ramana Rao V. R. Kompella

IPC: G06N20/20 ,  G06F16/901

CPC classification number: G06N20/20 ,  G06F16/9027

Abstract: In one embodiment, a controller for a federated learning system represents computing infrastructure for the federated learning system as a tree structure. The controller forms associations between datasets available to the federated learning system and nodes in the tree structure. The controller receives one or more instructions to perform model training in the federated learning system with datasets specified using their associations. The controller configures, in response to the one or more instructions, the federated learning system to perform the model training using the datasets specified by the one or more instructions using the tree structure.