公开(公告)号：US20230012451A1

公开(公告)日：2023-01-12

申请号：US17948222

申请日：2022-09-19

Applicant: Intel Corporation

Inventor： Justin E. Gottschlich ,  Naila Farooqui

IPC: G06N20/00

Abstract: Example methods and apparatus to generate anomaly detection datasets are disclosed. An example method to generate an anomaly detection dataset for training a machine learning model to detect real world anomalies includes receiving a user definition of an anomaly generator function, executing, with a processor, the anomaly generator function to generate user-defined anomaly data, and combining the user-defined anomaly data with nominal data to generate the anomaly detection dataset.

公开(公告)号：US20220327357A1

公开(公告)日：2022-10-13

申请号：US17723074

申请日：2022-04-18

Applicant: Intel Corporation

Inventor： Liwei Ma ,  Nadathur Rajagopalan Satish ,  Jeremy Bottleson ,  Farshad Akhbari ,  Eriko Nurvitadhi ,  Chandrasekaran Sakthivel ,  Barath Lakshmanan ,  Jingyi Jin ,  Justin E. Gottschlich ,  Michael Strickland

IPC: G06N3/04 ,  G06F9/50 ,  G06N3/063 ,  G06N3/08

Abstract: An apparatus to facilitate workload scheduling is disclosed. The apparatus includes one or more clients, one or more processing units to processes workloads received from the one or more clients, including hardware resources and scheduling logic to schedule direct access of the hardware resources to the one or more clients to process the workloads.

公开(公告)号：US11449786B2

公开(公告)日：2022-09-20

申请号：US15591886

申请日：2017-05-10

Applicant: Intel Corporation

Inventor： Justin E. Gottschlich ,  Naila Farooqui

IPC: G06N20/00

Abstract: Example methods and apparatus to generate anomaly detection datasets are disclosed. An example method to generate an anomaly detection dataset for training a machine learning model to detect real world anomalies includes receiving a user definition of an anomaly generator function, executing, with a processor, the anomaly generator function to generate user-defined anomaly data, and combining the user-defined anomaly data with nominal data to generate the anomaly detection dataset.

公开(公告)号：US11348198B2

公开(公告)日：2022-05-31

申请号：US17145885

申请日：2021-01-11

Applicant: Intel Corporation

Inventor： Prasoonkumar Surti ,  Narayan Srinivasa ,  Feng Chen ,  Joydeep Ray ,  Ben J. Ashbaugh ,  Nicolas C. Galoppo Von Borries ,  Eriko Nurvitadhi ,  Balaji Vembu ,  Tsung-Han Lin ,  Kamal Sinha ,  Rajkishore Barik ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Altug Koker ,  Nadathur Rajagopalan Satish ,  Farshad Akhbari ,  Dukhwan Kim ,  Wenyin Fu ,  Travis T. Schluessler ,  Josh B. Mastronarde ,  Linda L. Hurd ,  John H. Feit ,  Jeffery S. Boles ,  Adam T. Lake ,  Karthik Vaidyanathan ,  Devan Burke ,  Subramaniam Maiyuran ,  Abhishek R. Appu

IPC: G06T1/20 ,  G06F9/455 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F8/41

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a plurality of processing units each comprising a plurality of execution units (EUs), wherein the plurality of EUs comprise a first EU type and a second EU type.

公开(公告)号：US11210760B2

公开(公告)日：2021-12-28

申请号：US16928353

申请日：2020-07-14

Applicant: Intel Corporation

Inventor： Eriko Nurvitadhi ,  Balaji Vembu ,  Nicolas C. Galoppo Von Borries ,  Rajkishore Barik ,  Tsung-Han Lin ,  Kamal Sinha ,  Nadathur Rajagopalan Satish ,  Jeremy Bottleson ,  Farshad Akhbari ,  Altug Koker ,  Narayan Srinivasa ,  Dukhwan Kim ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Feng Chen ,  Elmoustapha Ould-Ahmed-Vall ,  Kevin Nealis ,  Xiaoming Chen ,  Anbang Yao

IPC: G06T1/20 ,  G06N3/04 ,  G06N3/063 ,  G06F9/38 ,  G06F9/30 ,  G06N3/08

Abstract: One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction, the decoded instruction to cause the compute apparatus to perform a complex machine learning compute operation.

公开(公告)号：US10956330B2

公开(公告)日：2021-03-23

申请号：US16727127

申请日：2019-12-26

Applicant: Intel Corporation

Inventor： Chandrasekaran Sakthivel ,  Prasoonkumar Surti ,  John C. Weast ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Abhishek R. Appu ,  Nicolas C. Galoppo Von Borries ,  Joydeep Ray ,  Narayan Srinivasa ,  Feng Chen ,  Ben J. Ashbaugh ,  Rajkishore Barik ,  Tsung-Han Lin ,  Kamal Sinha ,  Eriko Nurvitadhi ,  Balaji Vembu ,  Altug Koker

IPC: G06F12/0837 ,  G06N3/08 ,  G06N20/00 ,  G06T1/20 ,  G06F12/0815 ,  G06N3/063 ,  G06N3/04

Abstract: In an example, an apparatus comprises a plurality of processing unit cores, a plurality of cache memory modules associated with the plurality of processing unit cores, and a machine learning model communicatively coupled to the plurality of processing unit cores, wherein the plurality of cache memory modules share cache coherency data with the machine learning model. Other embodiments are also disclosed and claimed.

公开(公告)号：US10929749B2

公开(公告)日：2021-02-23

申请号：US15494948

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Narayan Srinivasa ,  Joydeep Ray ,  Nicolas C. Galoppo Von Borries ,  Ben Ashbaugh ,  Prasoonkumar Surti ,  Feng Chen ,  Barath Lakshmanan ,  Elmoustapha Ould-Ahmed-Vall ,  Liwei Ma ,  Linda L. Hurd ,  Abhishek R. Appu ,  John C. Weast ,  Sara S. Baghsorkhi ,  Justin E. Gottschlich ,  Chandrasekaran Sakthivel ,  Farshad Akhbari ,  Dukhwan Kim ,  Altug Koker ,  Nadathur Rajagopalan Satish

IPC: G06F9/30 ,  G06F9/38 ,  G06F9/28 ,  G06F9/302 ,  G06F15/00 ,  G06N3/08 ,  G06N3/04 ,  G06N3/063

Abstract: An apparatus to facilitate optimization of a neural network (NN) is disclosed. The apparatus includes optimization logic to define a NN topology having one or more macro layers, adjust the one or more macro layers to adapt to input and output components of the NN and train the NN based on the one or more macro layers.

公开(公告)号：US10719760B2

公开(公告)日：2020-07-21

申请号：US15482793

申请日：2017-04-09

Applicant: Intel Corporation

Inventor： Liwei Ma ,  Nadathur Rajagopalan Satish ,  Jeremy Bottleson ,  Farshad Akhbari ,  Eriko Nurvitadhi ,  Chandrasekaran Sakthivel ,  Barath Lakshmanan ,  Jingyi Jin ,  Justin E. Gottschlich ,  Michael Strickland

IPC: G06N3/04 ,  G06F9/50 ,  G06N3/063 ,  G06N3/08

Abstract: An apparatus to facilitate workload scheduling is disclosed. The apparatus includes one or more clients, one or more processing units to processes workloads received from the one or more clients, including hardware resources and scheduling logic to schedule direct access of the hardware resources to the one or more clients to process the workloads.

公开(公告)号：US10591313B2

公开(公告)日：2020-03-17

申请号：US15720718

申请日：2017-09-29

Applicant: Intel Corporation

Inventor： Lindsey Kuper ,  Justin E. Gottschlich

IPC: G01D1/10 ,  G07C5/00 ,  G01D3/08 ,  H04W4/04 ,  H04W4/70 ,  H04W4/38 ,  H04W4/40

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums for assessing the accuracy of one or more local sensors on a mobile device (such as an AV). In some examples, the accuracy of a local sensor of a first AV may be assessed by periodically comparing sensor readings collected by the local sensor to sensor readings matching selection criteria that are collected by one or more remote sensors located at one or more other nearby AVs. A sensor that reports data that is signifimaytly different from that reported by neighboring AVs is likely to be malfunctioning. The use of nearby sensors in nearby AVs may provide for a method for ensuring the integrity of the AV sensor readings without adding redundant sensors.

公开(公告)号：US20200019844A1

公开(公告)日：2020-01-16

申请号：US16518828

申请日：2019-07-22

Applicant: Intel Corporation

Inventor： Brian T. Lewis ,  Feng Chen ,  Jeffrey R. Jackson ,  Justin E. Gottschlich ,  Rajkishore Barik ,  Xiaoming Chen ,  Prasoonkumar Surti ,  Mike B. Macpherson ,  Murali Sundaresan

IPC: G06N3/063 ,  B60W30/095 ,  G06N3/00 ,  G06N3/04

Abstract: A mechanism is described for facilitating smart collection of data and smart management of autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and combining a first computation directed to be performed locally at a local computing device with a second computation directed to be performed remotely at a remote computing device in communication with the local computing device over the one or more networks, where the first computation consumes low power, wherein the second computation consumes high power.