Patents by Inventor Swaminathan Sundararaman

Swaminathan Sundararaman has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20220076166
    Abstract: Described herein are systems and methods for providing data sets from a constantly changing database to a streaming machine learning component. In one embodiment, a data streaming sub-system receives multiple incoming streams of data sets, in which each stream is generated in real-time by one of multiple data sources. The streaming sub-system sends data sets, on-the-fly as they are received, to storage in the memory of a database, in which there is a linkage between the storage and the time of arrival or the time of storage, of the data sets. The database receives, from a machine learning component, a request to receive data sets according to a particular time or time period. In response to such request, the database identifies such data sets according to the particular time or time period and sends them to the machine learning component.
    Type: Application
    Filed: November 15, 2021
    Publication date: March 10, 2022
    Applicant: DataRobot, Inc.
    Inventors: Swaminathan Sundararaman, Nisha Darshi Talagala, Gal Zuckerman
  • Patent number: 11176483
    Abstract: Described herein are systems and methods for providing data sets from a constantly changing database to a streaming machine learning component. In one embodiment, a data streaming sub-system receives multiple incoming streams of data sets, in which each stream is generated in real-time by one of multiple data sources. The streaming sub-system sends data sets, on-the-fly as they are received, to storage in the memory of a database, in which there is a linkage between the storage and the time of arrival or the time of storage, of the data sets. The database receives, from a machine learning component, a request to receive data sets according to a particular time or time period. In response to such request, the database identifies such data sets according to the particular time or time period and sends them to the machine learning component.
    Type: Grant
    Filed: May 3, 2017
    Date of Patent: November 16, 2021
    Assignee: DataRobot Inc.
    Inventors: Swaminathan Sundararaman, Nisha Darshi Talagala, Gal Zuckerman
  • Publication number: 20210049459
    Abstract: Described herein are systems and methods for executing efficiently, in real-time, a plurality of machine learning processes. In one embodiment, a computing platform with multiple compute elements receives multiple data streams, each such stream associated with its own respective machine learning process. Each machine learning process is operative to use its data stream as input to train, in real-time, a respective mathematical model. Each of the processes has peaks and dips in processing demands. The system re-allocates, in real-time, compute elements from the processes with lower processing demands to processes with higher processing demands, thereby handling all of the multiple processes on-the-fly, preventing peak demands from causing the system to stall, and reducing overall the computational resources required by the system.
    Type: Application
    Filed: April 28, 2020
    Publication date: February 18, 2021
    Inventors: Swaminathan Sundararaman, Lior Khermosh, Gal Zuckerman
  • Publication number: 20210026837
    Abstract: Apparatuses, systems, methods, and computer program products are disclosed for persistent memory management. Persistent memory management may include replicating a persistent data structure in volatile memory buffers of at least two non-volatile storage devices. Persistent memory management may include preserving a snapshot copy of data in association with completion of a barrier operation for the data. Persistent memory management may include determining which interface of a plurality of supported interfaces is to be used to flush data from a processor complex.
    Type: Application
    Filed: September 29, 2020
    Publication date: January 28, 2021
    Applicant: SanDisk Technologies LLC
    Inventors: Nisha Talagala, Swaminathan Sundararaman, David Flynn
  • Patent number: 10817502
    Abstract: Apparatuses, systems, methods, and computer program products are disclosed for persistent memory management. Persistent memory management may include providing a persistent data structure stored at least partially in volatile memory configured to ensure persistence of the data structure in a non-volatile memory medium. Persistent memory management may include replicating a persistent data structure in volatile memory buffers of at least two non-volatile storage devices. Persistent memory management may include preserving a snapshot copy of data in association with completion of a barrier operation for the data. Persistent memory management may include determining which interface of a plurality of supported interfaces is to be used to flush data from a processor complex.
    Type: Grant
    Filed: March 12, 2014
    Date of Patent: October 27, 2020
    Assignee: SANDISK TECHNOLOGIES LLC
    Inventors: Nisha Talagala, Swaminathan Sundararaman, David Flynn
  • Patent number: 10817421
    Abstract: Apparatuses, systems, methods, and computer program products are disclosed for a persistent data structure. A method includes associating a logical identifier with a data structure. A method includes writing data of a data structure to a first region of a volatile memory module. A volatile memory module may be configured to ensure that data is preserved in response to a trigger. A method includes copying data of a data structure from a volatile memory module to a non-volatile storage medium such that the data of the data structure remains associated with a logical identifier.
    Type: Grant
    Filed: March 12, 2014
    Date of Patent: October 27, 2020
    Assignee: SANDISK TECHNOLOGIES LLC
    Inventors: Nisha Talagala, Swaminathan Sundararaman, David Flynn
  • Publication number: 20200193313
    Abstract: Apparatuses, systems, program products, and methods are disclosed for interpretability-based machine learning adjustment during production. An apparatus includes a first results module that is configured to receive a first set of inference results of a first machine learning algorithm during inference of a production data set. An apparatus includes a second results module that is configured to receive a second set of inference results of a second machine learning algorithm during inference of a production data set. An apparatus includes an action module that is configured to trigger one or more actions that are related to a first machine learning algorithm in response to a comparison of first and second sets of inference results not satisfying explainability criteria.
    Type: Application
    Filed: December 14, 2018
    Publication date: June 18, 2020
    Applicant: Parallel Machines, Inc.
    Inventors: SINDHU GHANTA, DREW ROSELLI, NISHA TALAGALA, VINAY SRIDHAR, SWAMINATHAN SUNDARARAMAN, LIOR AMAR, LIOR KHERMOSH, BHARATH RAMSUNDAR, SRIRAM SUBRAMANIAN
  • Patent number: 10671916
    Abstract: Described herein are systems and methods for executing efficiently, in real-time, a plurality of machine learning processes. In one embodiment, a computing platform with multiple compute elements receives multiple data streams, each such stream associated with its own respective machine learning process. Each machine learning process is operative to use its data stream as input to train, in real-time, a respective mathematical model. Each of the processes has peaks and dips in processing demands. The system re-allocates, in real-time, compute elements from the processes with lower processing demands to processes with higher processing demands, thereby handling all of the multiple processes on-the-fly, preventing peak demands from causing the system to stall, and reducing overall the computational resources required by the system.
    Type: Grant
    Filed: September 20, 2016
    Date of Patent: June 2, 2020
    Assignee: DataRobot, Inc.
    Inventors: Swaminathan Sundararaman, Lior Khermosh, Gal Zuckerman
  • Publication number: 20200034665
    Abstract: Apparatuses, systems, program products, and methods are disclosed for determining validity of machine learning algorithms for datasets. An apparatus includes a primary training module that is configured to train a first machine learning model for a first machine learning algorithm. An apparatus includes a primary validation module that is configured to validate a first machine learning model to generate an error data set. An apparatus includes a secondary training module that is configured to train a second machine learning model for a second machine learning algorithm using an error data set. A second machine learning algorithm may be configured to predict a suitability of a first machine learning model for analyzing an inference data set. An apparatus includes an action module that is configured to trigger an action in response to a predicted suitability of the first machine learning model not satisfying a predetermined suitability threshold.
    Type: Application
    Filed: July 30, 2018
    Publication date: January 30, 2020
    Applicant: DataRobot, Inc.
    Inventors: SINDHU GHANTA, DREW ROSELLI, NISHA TALAGALA, VINAY SRIDHAR, SWAMINATHAN SUNDARARAMAN, LIOR AMAR, LIOR KHERMOSH, BHARATH RAMSUNDAR, SRIRAM SUBRAMANIAN
  • Patent number: 10509776
    Abstract: An apparatus, system, and method are disclosed for data management. The method includes writing data in a sequential log structure. The method also includes receiving a time sequence request from a client. The method further includes servicing the time sequence request based on a temporal order of the data in the sequential log structure.
    Type: Grant
    Filed: March 14, 2013
    Date of Patent: December 17, 2019
    Assignee: SANDISK TECHNOLOGIES LLC
    Inventors: Nisha Talagala, Swaminathan Sundararaman, Sriram Subramanian, James Peterson, David Flynn
  • Publication number: 20190377984
    Abstract: Apparatuses, systems, program products, and method are disclosed for detecting suitability of machine learning models for datasets. An apparatus includes a training evaluation module configured to calculate a first statistical data signature for a training data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes an inference evaluation module configured to calculate a second statistical data signature for an inference data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes a score module configured to calculate a suitability score describing the suitability of a training data set to an inference data set as a function of a first and a second statistical data signature. An apparatus includes an action module configured to perform an action related to a machine learning system in response to a suitability score satisfying an unsuitability threshold.
    Type: Application
    Filed: June 6, 2018
    Publication date: December 12, 2019
    Applicant: DataRobot, Inc.
    Inventors: SINDHU GHANTA, DREW ROSELLI, NISHA TALAGALA, VINAY SRIDHAR, SWAMINATHAN SUNDARARAMAN, LIOR AMAR, LIOR KHERMOSH, BHARATH RAMSUNDAR, SRIRAM SUBRAMANIAN
  • Patent number: 10425325
    Abstract: Embodiments are directed to a process and system for optimizing traffic paths for orphaned hosts in a VXLAN system, by configuring virtual link trunking (VLT) peers to advertise MAC addresses learned from all multi-homed hosts in the system using Anycast VXLAN tunnel endpoint-Internet Protocol address (VTEP-IP); configuring the virtual link trunking (VLT) peers to advertise MAC addresses learned from all single-homed hosts in the system using a secondary VTEP-IP; directing unicast traffic destined to the single-homed hosts to directly connected VLT peers using the Secondary VTEP-IP; and directing Broadcast, unknown unicast, and multi-cast (BUM) traffic destined to the single-homed hosts to directly connected VLT peers using the Inclusive Multicast Ethernet Tag route.
    Type: Grant
    Filed: October 30, 2017
    Date of Patent: September 24, 2019
    Assignee: Dell Products LP
    Inventors: Phaniraj Vattem, Mukesh Moopath Velayudhan, Anoop Ghanwani, Swaminathan Sundararaman, Mohan Ayalasomayajula, Bhavini Gada
  • Publication number: 20190251067
    Abstract: Apparatuses, systems, and methods are disclosed for snapshots of a non-volatile device. A method includes writing data in a sequential log structure for a non-volatile device. A method includes marking a point, in a sequential log structure, for a snapshot of data. A method includes preserving a logical-to-physical mapping for a snapshot based on a marked point and a temporal order for data in a sequential log structure.
    Type: Application
    Filed: April 25, 2019
    Publication date: August 15, 2019
    Applicant: SanDisk Technologies LLC
    Inventors: Nisha Talagala, Swaminathan Sundararaman, Sriram Subramanian
  • Patent number: 10380026
    Abstract: A storage system implements a sparse, thinly provisioned logical-to-physical translation layer. The storage system may perform operations to modify logical-to-physical mappings, including creating, removing, and/or modifying any-to-any and/or many-to-one mappings between logical identifiers and stored data (logical manipulation operations). The storage system records persistent metadata to render the logical manipulation (LM) operations persistent and crash-safe. The storage system may provide access to LM functionality through a generalized LM interface. Clients may leverage the LM interface to efficiently implement higher-level functionality and/or offload LM operations to the storage system.
    Type: Grant
    Filed: December 12, 2014
    Date of Patent: August 13, 2019
    Assignee: SANDISK TECHNOLOGIES LLC
    Inventors: Swaminathan Sundararaman, Nisha Talagala, Robert Wipfel, Sriram Subramanian, Vladislav Bolkhovitin
  • Patent number: 10318495
    Abstract: Apparatuses, systems, and methods are disclosed for snapshots of a non-volatile device. A method includes writing data in a sequential log structure for a non-volatile device. A method includes marking a point, in a sequential log structure, for a snapshot of data. A method includes preserving a logical-to-physical mapping for a snapshot based on a marked point and a temporal order for data in a sequential log structure.
    Type: Grant
    Filed: July 11, 2013
    Date of Patent: June 11, 2019
    Assignee: SANDISK TECHNOLOGIES LLC
    Inventors: Nisha Talagala, Swaminathan Sundararaman, Sriram Subramanian
  • Patent number: 10289545
    Abstract: Apparatuses, systems, methods, and computer program products are disclosed for hybrid checkpointed memory. An extended memory module uses volatile memory of a host and a non-volatile memory medium as virtual memory for the host. A clone module clones data of a range of virtual memory in response to a checkpoint event for the range of virtual memory. A range of virtual memory may include data stored in a volatile memory and data stored in a non-volatile memory medium. A checkpoint module flushes dirty data of a range of virtual memory to a non-volatile memory medium in response to a checkpoint event. A hybrid checkpointed memory interface provides access to data of a range of virtual memory while dirty data is being flushed using data of a range of virtual memory, or using a clone of the data.
    Type: Grant
    Filed: May 31, 2017
    Date of Patent: May 14, 2019
    Assignee: SANDISK TECHNOLOGIES LLC
    Inventors: Nisha Talagala, Swaminathan Sundararaman, Nick Piggin, Ashish Batwara, David Flynn
  • Publication number: 20190132241
    Abstract: Embodiments are directed to a process and system for optimizing traffic paths for orphaned hosts in a VXLAN system, by configuring virtual link trunking (VLT) peers to advertise MAC addresses learned from all multi-homed hosts in the system using Anycast VXLAN tunnel endpoint-Internet Protocol address (VTEP-IP); configuring the virtual link trunking (VLT) peers to advertise MAC addresses learned from all single-homed hosts in the system using a secondary VTEP-IP; directing unicast traffic destined to the single-homed hosts to directly connected VLT peers using the Secondary VTEP-IP; and directing Broadcast, unknown unicast, and multi-cast (BUM) traffic destined to the single-homed hosts to directly connected VLT peers using the Inclusive Multicast Ethernet Tag route.
    Type: Application
    Filed: October 30, 2017
    Publication date: May 2, 2019
    Inventors: Phaniraj Vattem, Mukesh Moopath Velayudhan, Anoop Ghanwani, Swaminathan Sundararaman, Mohan Ayalasomayajula, Bhavini Gada
  • Publication number: 20190108417
    Abstract: Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.
    Type: Application
    Filed: June 5, 2018
    Publication date: April 11, 2019
    Applicant: Parallel Machines, Inc.
    Inventors: NISHA TALAGALA, VINAY SRIDHAR, SWAMINATHAN SUNDARARAMAN, SINDHU GHANTA, LIOR AMAR, LIOR KHERMOSH, BHARATH RAMSUNDAR, SRIRAM SUBRAMANIAN, DREW ROSELLI
  • Patent number: 10102144
    Abstract: A data services module performs log storage operations in response to requests by storing data on one or more storage devices, and appending information pertaining to the requests to a separate metadata log. A log order of the metadata log may correspond to an order in which the requests were received, regardless of the order in which data of the requests are written to the storage devices. The requests may correspond to identifiers of a logical address space. The data services module implements an any-to-any translation layer configured to map identifiers of the logical address space to the stored data. The virtualization module may include a metadata management module configured to checkpoint the translation layer metadata by, inter alia, appending aggregate, checkpoint entries to the metadata log. The data services module may leverage the translation layer between the logical identifiers and underlying storage locations to efficiently implement logical manipulation operations.
    Type: Grant
    Filed: April 15, 2014
    Date of Patent: October 16, 2018
    Assignee: SANDISK TECHNOLOGIES LLC
    Inventors: Swaminathan Sundararaman, Nisha Talagala, Sriram Subramanian
  • Patent number: 10102117
    Abstract: A cache module leverages storage metadata to cache data of a backing store on a non-volatile storage device. The cache module maintains access metadata pertaining to access characteristics of logical identifiers in the logical address space, including access characteristics of un-cached logical identifiers (e.g., logical identifiers associated with data that is not stored on the non-volatile storage device). The access metadata may be separate and/or distinct from the storage metadata. The cache module determines whether to admit data into the cache and/or evict data from the cache using the access metadata. A storage module may provide eviction candidates to the cache module. The cache module may select candidates for eviction. The storage module may leverage the eviction candidates to improve the performance of storage recovery and/or grooming operations.
    Type: Grant
    Filed: February 22, 2013
    Date of Patent: October 16, 2018
    Assignee: SANDISK TECHNOLOGIES LLC
    Inventors: Nisha Talagala, Swaminathan Sundararaman