Patent search ap:("Intel Corporation") AND inv:"Jeremy Bottleson" Page 3

21.

发明申请
DYNAMIC DISTRIBUTED TRAINING OF MACHINE LEARNING MODELS 审中-公开

公开(公告)号：US20180307984A1

公开(公告)日：2018-10-25

申请号：US15494971

申请日：2017-04-24

Applicant: Intel Corporation

Inventor： Altug Koker , Abhishek R. Appu , Kamal Sinha , Joydeep Ray , Balaji Vembu , Elmoustapha Ould-Ahmed-Vall , Sara S. Baghsorkhi , Anbang Yao , Kevin Nealis , Xiaoming Chen , John C. Weast , Justin E. Gottschlich , Prasoonkumar Surti , Chandrasekaran Sakthivel , Farshad Akhbari , Nadathur Rajagopalan Satish , Liwei Ma , Jeremy Bottleson , Eriko Nurvitadhi , Travis T. Schluessler , Ankur N. Shah , Jonathan Kennedy , Vasanth Ranganathan , Sanjeev Jahagirdar

IPC: G06N3/08 , G06N99/00

CPC classification number: G06N3/08 , G06F9/28 , G06F9/505 , G06N3/0445 , G06N3/0454 , G06N3/0481 , G06N3/063 , G06N99/005

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.

22.

发明申请
CONVOLUTIONAL NEURAL NETWORK OPTIMIZATION MECHANISM 审中-公开

公开(公告)号：US20180300600A1

公开(公告)日：2018-10-18

申请号：US15488551

申请日：2017-04-17

Applicant: Intel Corporation

Inventor： Liwei Ma , Elmoustapha Ould- Ahmed-Vall , Barath Lakshmanan , Ben J. Ashbaugh , Jingyi Jin , Jeremy Bottleson , Mike B. Macpherson , Kevin Nealis , Dhawal Srivastava , Joydeep Ray , Ping T. Tang , Michael S. Strickland , Xiaoming Chen , Anbang Yao , Tatiana Shpeisman , Altug Koker , Abhishek R. Appu

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

Abstract: An apparatus to facilitate optimization of a convolutional neural network (CNN) is disclosed. The apparatus includes optimization logic to receive a CNN model having a list of instructions and including pruning logic to optimize the list of instructions by eliminating branches in the list of instructions that comprise a weight value of 0.

23.

发明申请
PROGRAMMABLE COARSE GRAINED AND SPARSE MATRIX COMPUTE HARDWARE WITH ADVANCED SCHEDULING 有权

公开(公告)号：US20250061534A1

公开(公告)日：2025-02-20

申请号：US18819073

申请日：2024-08-29

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 , G06F9/30 , G06F9/38 , G06N3/04 , G06N3/044 , G06N3/045 , G06N3/063 , G06N3/08 , G06N3/084

Abstract: One embodiment provides a parallel processor comprising a hardware scheduler to schedule pipeline commands for compute operations to one or more of multiple types of compute units, a plurality of processing resources including a first sparse compute unit configured for input at a first level of sparsity and hybrid memory circuitry including a memory controller, a memory interface, and a second sparse compute unit configured for input at a second level of sparsity that is greater than the first level of sparsity.

24.

发明公开
CONVOLUTIONAL NEURAL NETWORK OPTIMIZATION MECHANISM 审中-公开

公开(公告)号：US20240256825A1

公开(公告)日：2024-08-01

申请号：US18435528

申请日：2024-02-07

Applicant: Intel Corporation

Inventor： Liwei Ma , Elmoustapha Ould-Ahmed-Vall , Barath Lakshmanan , Ben J. Ashbaugh , Jingyi Jin , Jeremy Bottleson , Mike B. Macpherson , Kevin Nealis , Dhawal Srivastava , Joydeep Ray , Ping T. Tang , Michael S. Strickland , Xiaoming Chen , Anbang Yao , Tatiana Shpeisman , Altug Koker , Abhishek R. Appu

IPC: G06N3/04 , G06N3/044 , G06N3/045 , G06N3/063 , G06N3/082 , G06T1/20

CPC classification number: G06N3/04 , G06N3/063 , G06N3/082 , G06T1/20 , G06N3/044 , G06N3/045

Abstract: A library of machine learning primitives is provided to optimize a machine learning model to improve the efficiency of inference operations. In one embodiment a trained convolutional neural network (CNN) model is processed into a trained CNN model via pruning, convolution window optimization, and quantization.

25.

发明公开
PRIVACY-PRESERVING SEARCH USING HOMOMORPHIC ENCRYPTION 审中-公开

公开(公告)号：US20240104224A1

公开(公告)日：2024-03-28

申请号：US17935826

申请日：2022-09-27

Applicant: Intel Corporation

Inventor： Ernesto Zamora Ramos , Kylan Race , Jeremy Bottleson , Jingyi Jin

IPC: G06F21/60 , H04L9/00 , H04L9/08

CPC classification number: G06F21/602 , H04L9/008 , H04L9/0825

Abstract: An improved search operation includes receiving, by a server computing device, an encrypted search query and cleartext metadata associated with the encrypted search query from a client computing device; performing a search using the encrypted search query to generate encrypted search results; and sending the encrypted search results to the client computing device.

26.

发明公开
PROGRAMMABLE COARSE GRAINED AND SPARSE MATRIX COMPUTE HARDWARE WITH ADVANCED SCHEDULING 审中-公开

公开(公告)号：US20230394616A1

公开(公告)日：2023-12-07

申请号：US18334733

申请日：2023-06-14

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 , G06N3/063 , G06F9/38 , G06F9/30 , G06N3/084 , G06N3/044 , G06N3/045 , G06N3/04 , G06N3/08

CPC classification number: G06T1/20 , G06N3/063 , G06F9/3887 , G06F9/3895 , G06F9/3001 , G06F9/3851 , G06F9/3017 , G06N3/084 , G06N3/044 , G06N3/045 , G06N3/04 , G06N3/08

Abstract: One embodiment provides a parallel processor comprising a hardware scheduler to schedule pipeline commands for compute operations to one or more of multiple types of compute units, a plurality of processing resources including a first sparse compute unit configured for input at a first level of sparsity and hybrid memory circuitry including a memory controller, a memory interface, and a second sparse compute unit configured for input at a second level of sparsity that is greater than the first level of sparsity.

27.

发明申请
PROGRAMMABLE COARSE GRAINED AND SPARSE MATRIX COMPUTE HARDWARE WITH ADVANCED SCHEDULING 有权

公开(公告)号：US20220164916A1

公开(公告)日：2022-05-26

申请号：US17541413

申请日：2021-12-03

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 compute operation.

28.

发明授权
Neural network scheduling mechanism 有权

公开(公告)号：US11315007B2

公开(公告)日：2022-04-26

申请号：US16918220

申请日：2020-07-01

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 Strikland

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.