公开(公告)号：US20210160267A1

公开(公告)日：2021-05-27

申请号：US17024884

申请日：2020-09-18

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Sergey SEREBRYAKOV ,  Tahir CADER ,  Nanjundaiah DEEPAK

IPC: H04L29/06 ,  H04L12/26

Abstract: An example device includes processing circuitry and a memory. The memory includes instructions that cause the device to perform various functions. The functions include receiving datastreams from a plurality of sensors of a high performance computing system, classifying each datastream of the each sensor to one of a plurality of datastream models, selecting an anomaly detection algorithm from a plurality of anomaly detection algorithms for each datastream, determining parameters of the each anomaly detection algorithm, determining an anomaly threshold for each datastream, and generating an indication that the sensor associated with the datastream is acting anomalously.

公开(公告)号：US20210241068A1

公开(公告)日：2021-08-05

申请号：US17049032

申请日：2018-04-30

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Martin FOLTIN ,  John Paul STRACHAN ,  Sergey SEREBRYAKOV

IPC: G06N3/04 ,  G06N3/10 ,  G06N3/08

Abstract: A convolutional neural network system includes a first part of the convolutional neural network comprising an initial processor configured to process an input data set and store a weight factor set in the first part of the convolutional neural network; and a second part of the convolutional neural network comprising a main computing system configured to process an export data set provided from the first part of the convolutional neural network.

公开(公告)号：US20240046988A1

公开(公告)日：2024-02-08

申请号：US17876471

申请日：2022-07-28

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： GIACOMO PEDRETTI ,  Catherine GRAVES ,  Sergey SEREBRYAKOV ,  John Paul STRACHAN

IPC: G11C15/04 ,  G11C7/06 ,  G11C7/16 ,  H03M1/18

CPC classification number: G11C15/04 ,  G11C7/062 ,  G11C7/16 ,  H03M1/181

Abstract: Embodiments of the disclosure provide a system, method, or computer readable medium for providing a differentiable content addressable memory (aCAM) that implements an analog input analog storage and analog output learning memory. The analog output of the differentiable CAM can provide input to a learning algorithm, which may compute the gradients in comparison to historic values and reduce data inaccuracies and power consumption.

公开(公告)号：US20220300712A1

公开(公告)日：2022-09-22

申请号：US17209174

申请日：2021-03-22

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Suparna BHATTACHARYA ,  Mayukh DUTTA ,  Manoj SRIVATSAV ,  Sergey SEREBRYAKOV

IPC: G06F40/30 ,  G06N5/04 ,  G06N20/00 ,  G06F40/295 ,  G06T11/20

Abstract: Artificial-intelligence (AI)-based question-answer (QA) trace analysis of a text corpus that identifies answers to a natural language question and assesses the manner in which those answers evolve over time based on associated context is described herein. A set of QA trace records can be generated that includes a collection of answers derived from a text corpus in response to a posed natural language question along with contextual information relating to the answers. The set of QA trace records can be ordered based on corresponding date attributes gleaned from the contextual information to produce a time-series of QA trace records that can be processed by various types of downstream processing. Such downstream processing can include data visualization, pattern recognition, or the like for assessing how an answer to a natural language question evolves over time, identifying patterns/trends that develop over time with respect to the set of answers, and so forth.