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公开(公告)号：US20200004596A1
公开(公告)日：2020-01-02
申请号：US16020776
申请日：2018-06-27
Applicant: Amazon Technologies, Inc.
Inventor： Sudipta SENGUPTA , Poorna Chand Srinivas PERUMALLA , Dominic Rajeev DIVAKARUNI , Nafea BSHARA , Leo Parker DIRAC , Bratin SAHA , Matthew James WOOD , Andrea OLGIATI , Swaminathan SIVASUBRAMANIAN
Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes receiving an application instance configuration, an application of the application instance to utilize a portion of an attached accelerator during execution of a machine learning model and the application instance configuration including: an indication of the central processing unit (CPU) capability to be used, an arithmetic precision of the machine learning model to be used, an indication of the accelerator capability to be used, a storage location of the application, and an indication of an amount of random access memory to use.
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Patent Agency Ranking
公开(公告)号：US20190156124A1
公开(公告)日：2019-05-23
申请号：US15926745
申请日：2018-03-20
Applicant: Amazon Technologies, Inc.
Inventor： Nitin SINGHAL , Vivek BHADAURIA , Ranju DAS , Gaurav D. GHARE , Roman GOLDENBERG , Stephen GOULD , Kuang HAN , Jonathan Andrew HEDLEY , Gowtham JEYABALAN , Vasant MANOHAR , Andrea OLGIATI , Stefano STEFANI , Joseph Patrick TIGHE , Praveen Kumar Udayakumar , Renjun ZHANG
IPC: G06K9/00
CPC classification number: G06K9/00744 , G06F16/71 , G06K9/00228 , G06K9/00718 , G06K9/00765 , G06K2009/00738
Abstract: Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.
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公开(公告)号：US20200004597A1
公开(公告)日：2020-01-02
申请号：US16020810
申请日：2018-06-27
Applicant: Amazon Technologies, Inc.
Inventor： Sudipta SENGUPTA , Poorna Chand Srinivas PERUMALLA , Dominic Rajeev DIVAKARUNI , Nafea BSHARA , Leo Parker DIRAC , Bratin SAHA , Matthew James WOOD , Andrea OLGIATI , Swaminathan SIVASUBRAMANIAN
Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes provisioning an application instance and portions of at least one accelerator attached to the application instance to execute a machine learning model of an application of the application instance; loading the machine learning model onto the portions of the at least one accelerator; receiving scoring data in the application; and utilizing each of the portions of the attached at least one accelerator to perform inference on the scoring data in parallel and only using one response from the portions of the accelerator
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公开(公告)号：US20200004595A1
公开(公告)日：2020-01-02
申请号：US16020819
申请日：2018-06-27
Applicant: Amazon Technologies, Inc.
Inventor： Sudipta SENGUPTA , Poorna Chand Srinivas PERUMALLA , Dominic Rajeev DIVAKARUNI , Nafea BSHARA , Leo Parker DIRAC , Bratin SAHA , Matthew James WOOD , Andrea OLGIATI , Swaminathan SIVASUBRAMANIAN
Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes attaching a first set of one or more accelerator slots of an accelerator appliance to an application instance of a multi-tenant provider network according to an application instance configuration, the application instance configuration to define per accelerator slot capabilities to be used by an application of the application instance, wherein the multi-tenant provider network comprises a plurality of computing devices configured to implement a plurality of virtual compute instances, and wherein the first set of one or more accelerator slots is implemented using physical accelerator resources accessible to the application instance; while performing inference using the loaded machine learning model of the application using the first set of one or more accelerator slots on the attached accelerator appliance, managing resources of the accelerator appliance using an accelerator appliance manager of the accelerator appliance.
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公开(公告)号：US20200160050A1
公开(公告)日：2020-05-21
申请号：US16198040
申请日：2018-11-21
Applicant: Amazon Technologies, Inc.
Inventor： Rahul BHOTIKA , Shai MAZOR , Amit ADAM , Wendy TSE , Andrea OLGIATI , Bhavesh DOSHI , Gururaj KOSURU , Patrick Ian WILSON , Umar FAROOQ , Anand DHANDHANIA
IPC: G06K9/00
Abstract: Techniques for layout-agnostic complex document processing are described. A document processing service can analyze documents that do not adhere to defined layout rules in an automated manner to determine the content and meaning of a variety of types of segments within the documents. The service may chunk a document into multiple chunks, and operate upon the chunks in parallel by identifying segments within each chunk, classifying the segments into segment types, and processing the segments using special-purpose analysis engines adapted for the analysis of particular segment types to generate results that can be aggregated into an overall output for the entire document that captures the meaning and context of the document text.
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公开(公告)号：US20200005124A1
公开(公告)日：2020-01-02
申请号：US16020788
申请日：2018-06-27
Applicant: Amazon Technologies, Inc.
Inventor： Sudipta SENGUPTA , Poorna Chand Srinivas PERUMALLA , Dominic Rajeev DIVAKARUNI , Nafea BSHARA , Leo Parker DIRAC , Bratin SAHA , Matthew James WOOD , Andrea OLGIATI , Swaminathan SIVASUBRAMANIAN
Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes receiving an application instance configuration, an application of the application instance to utilize a portion of an attached accelerator during execution of a machine learning model and the application instance configuration including an arithmetic precision of the machine learning model to be used in determining the portion of the accelerator to provision; provisioning the application instance and the portion of the accelerator attached to the application instance, wherein the application instance is implemented using a physical compute instance in a first location, wherein the portion of the accelerator is implemented using a physical accelerator in the second location; loading the machine learning model onto the portion of the accelerator; and performing inference using the loaded machine learning model of the application using the portion of the accelerator on the attached accelerator.
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