Patents by Inventor Rekha Singhal

Rekha Singhal 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: 20240235961
    Abstract: Cloud and Fog computing are complementary technologies used for complex Internet of Things (IoT) based deployment of applications. With an increase in the number of internet-connected devices, the volume of data generated and processed at higher speeds has increased substantially. Serving a large amount of data and workloads for predictive decisions in real-time using fog computing without Service-Level Objective (SLO) violation is a challenge. Present disclosure provides systems and method for inference management wherein a suitable execution workflow is automatically generated to execute machine learning (ML)/deep learning (DL) inference requests using fog with various type of instances (e.g., Function-as-a-Service (FaaS) instance, Machine Learning-as-a-service (MLaaS) instance, and the like) provided by cloud vendors/platforms. Generated workflow minimizes the cost of deployment as well as SLO violations.
    Type: Application
    Filed: December 21, 2023
    Publication date: July 11, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: CHETAN DNYANDEO PHALAK, DHEERAJ CHAHAL, REKHA SINGHAL
  • Publication number: 20240220245
    Abstract: Data processing code in machine learning pipelines is primarily done using data frame APIs provided by Pandas and similar libraries. Though, these libraries are easy to use, their temporal performance is worse than similar code written using NumPy or other high-performance libraries. Embodiments herein provide a system and method for acceleration of slower data processing code in machine learning pipelines by automatically generating an accelerated data processing code. Initially, a code is received and pre-processed based on a predefined format to get a standardized code. Further, system identifies code statements having operations that to be performed on a data frame, and an ordered list of data frame columns to generate a filtered dictionary code. Further, a data processing representation is generated using filtering dictionary code and ordered list of data frame columns. Finally, an accelerated data processing code is recommended based on the data processing representation.
    Type: Application
    Filed: December 19, 2023
    Publication date: July 4, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Mayank Mishra, Rekha Singhal
  • Publication number: 20240160949
    Abstract: Technical limitation of conventional Gradient-Based Meta Learners is their inability to adapt to scenarios where input tasks are sampled from multiple distributions. Training multiple models, with one model per distribution adds to the training time owing to increased compute. A method and system for generating meta-subnets for efficient model generalization in a multi-distribution scenario using Binary Mask Perceptron (BMP) technique or a Multi-modal Meta Supermasks (MMSUP) technique is provided. The BMP utilizes an adaptor which determines a binary mask, thus training only those layers which are relevant for given input distribution, leading to improved training accuracy in a cross-domain scenario. The MMSUP, further determines relevant subnets for each input distribution, thus, generalizing well as compared to standard MAML. The BMP and MMSUP, beat Multi-MAML in terms of training time as they train a single model on multiple distributions as opposed to Multi-MAML which trains multiple models.
    Type: Application
    Filed: August 23, 2023
    Publication date: May 16, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Shruti Kunal KUNDE, Rekha SINGHAL, Varad Anant PIMPALKHUTE
  • Publication number: 20240119008
    Abstract: Works in the literature fail to leverage embedding access patterns and memory units' access/storage capabilities, which when combined can yield high-speed heterogeneous systems by dynamically re-organizing embedding tables partitions across hardware during inference. A method and system for optimal deployment of embeddings tables across heterogeneous memory architecture for high-speed recommendations inference is disclosed, which dynamically partitions and organizes embedding tables across fast memory architectures to reduce access time. Partitions are chosen to take advantage of the past access patterns of those tables to ensure that frequently accessed data is available in the fast memory most of the time. Partition and replication is used to co-optimize memory access time and resources.
    Type: Application
    Filed: August 25, 2023
    Publication date: April 11, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Ashwin KRISHNAN, Manoj Karunakaran Nambiar, Chinmay Narendra Mahajan, Rekha Singhal
  • Publication number: 20240112095
    Abstract: The disclosure generally relates to an FPGA-based online 3D bin packing. Online 3D bin packing is the process of packing boxes into larger bins-Long Distance Containers (LDCs) such that the space inside each LDC is used to the maximum extent. The use of deep reinforcement learning (Deep RL) for this process is effective and popular. However, since the existing processor-based implementations are limited by Von-Neumann architecture and take a long time to evaluate each alignment for a box, only a few potential alignments are considered, resulting in sub-optimal packing efficiency. This disclosure describes an architecture for bin packing which leverages pipelining and parallel processing on FPGA for faster and exhaustive evaluation of all alignments for each box resulting in increased efficiency. In addition, a suitable generic purpose processor is employed to train the neural network within the algorithm to make the disclosed techniques computationally light, faster and efficient.
    Type: Application
    Filed: August 25, 2023
    Publication date: April 4, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: ASHWIN KRISHNAN, HARSHAD KHADILKAR, REKHA SINGHAL, ANSUMA BASUMATARY, MANOJ KARUNAKARAN NAMBIAR, ARIJIT MUKHERJEE, KAVYA BORRA
  • Publication number: 20240070540
    Abstract: Existing approaches for switching between different hardware accelerators in a heterogeneous accelerator approach have the disadvantage that complete potential of the heterogeneous hardware accelerators do not get used as the switching relies on load on the accelerators or a random switching in which entire task gets reassigned to a different hardware accelerator. The disclosure herein generally relates to data model training, and, more particularly, to a method and system for data model training using heterogeneous hardware accelerators. In this approach, the system switches between hardware accelerators when a measured accuracy of the data model after any epoch is below a threshold of accuracy.
    Type: Application
    Filed: July 31, 2023
    Publication date: February 29, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: MAYANK MISHRA, RAVI KUMAR SINGH, REKHA SINGHAL
  • Publication number: 20240062045
    Abstract: This disclosure relates generally to a method and system for latency optimized heterogeneous deployment of convolutional neural network (CNN). State-of-the-art methods for optimal deployment of convolutional neural network provide a reasonable accuracy. However, for unseen networks the same level of accuracy is not attained. The disclosed method provides an automated and unified framework for the convolutional neural network (CNN) that optimally partitions the CNN and maps these partitions to hardware accelerators yielding a latency optimized deployment configuration. The method provides an optimal partitioning of the CNN for deployment on heterogeneous hardware platforms by searching network partition and hardware pair optimized for latency while including communication cost between hardware. The method employs performance model-based optimization algorithm to optimally deploy components of a deep learning pipeline across right heterogeneous hardware for high performance.
    Type: Application
    Filed: July 27, 2023
    Publication date: February 22, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Nupur SUMEET, Manoj Karunakaran NAMBIAR, Rekha SINGHAL, Karan RAWAT
  • Publication number: 20230419180
    Abstract: Hardly any work in literature attempts employing Function-as-a-Service (FaaS) or serverless architecture to accelerate the training or re-training process of meta-learning architectures. Embodiments of the present disclosure provide a method and system for meta learning using distributed training on serverless architecture. The system, interchangeably referred to as MetaFaaS, is a meta-learning based scalable architecture using serverless distributed setup. Hierarchical nature of gradient based architectures is leveraged to facilitate distributed training on the serverless architecture. Further, a compute-efficient architecture, efficient Adaptive Learning of hyperparameters for Fast Adaptation (eALFA) for meta-learning is provided. The serverless architecture based training of models during meta learning enables unlimited scalability and reduction of training time by using optimal number of serverless instances.
    Type: Application
    Filed: April 3, 2023
    Publication date: December 28, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: SHRUTI KUNAL KUNDE, VARAD ANANT PIMPALKHUTE, REKHA SINGHAL
  • Publication number: 20230421504
    Abstract: Heterogeneous cloud storage services offered by different cloud service providers have unique deliverable performance. One key challenge is to find the maximum achievable data transfer rate from one cloud service to another. The disclosure herein generally relates to cloud computing, and, more particularly, to a method and system for parameter tuning in cloud network. The system obtains optimum value of parameters of a source cloud and a destination cloud in a cloud pair, by performing a parameter tuning. The optimum value of parameters and corresponding data transfer rate is used as a training data to generate a data model. The data model processes real-time information with respect to cloud pairs, and predicts corresponding data transfer rate.
    Type: Application
    Filed: May 23, 2023
    Publication date: December 28, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: DHEERAJ CHAHAL, SURYA CHAITANYA VENKATA PALEPU, MAYANK MISHRA, REKHA SINGHAL, MANJU RAMESH
  • Publication number: 20230409967
    Abstract: State of the art methods require size of DL model, or its gradients be less than maximum data item size of storage used as a communication channel for model training with serverless platform. Embodiments of the present disclosure provide method and system for training large DL models via serverless architecture using communication channel when the gradients are larger than maximum size of one data item allowed by the channel. Gradients that are generated by each worker during current training instance, are chunked into segments and stored in the communication channel. Corresponding segments of each worker are aggregated by aggregators and stored back. Each of the aggregated corresponding segments are read by each worker to generate an aggregated model to be used during successive training instance. Optimization techniques are used for reading-from and writing-to the channel resulting in significant improvement in performance and cost of training.
    Type: Application
    Filed: April 27, 2023
    Publication date: December 21, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: Dheeraj CHAHAL, Surya Chaitanya Venkata PALEPU, Mayank MISHRA, Ravi Kumar SINGH, Rekha SINGHAL
  • Patent number: 11775264
    Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.
    Type: Grant
    Filed: September 2, 2021
    Date of Patent: October 3, 2023
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Rekha Singhal, Mayank Mishra, Dheeraj Chahal, Shruti Kunde, Manju Ramesh
  • Publication number: 20230305544
    Abstract: Large training times incurred during the process of self-learning of ML models in digital twins are debilitating and can adversely affect the functioning of industrial plants. Embodiments of the present disclosure provide a method and system for accelerated self-learning using application agnostic meta learner trained using optimal set of meta features selected from classification meta features, regression meta features, and domain meta features based on a domain-meta-feature-taxonomy created for a plurality of industrial plants across a plurality of domains. Optimal feature selection is enabled using ML, DL that provides static feature selection, while Q-learning based approach is disclosed that enables dynamic feature selection. Q-learning based approach has two implementations, static and dynamic reward.
    Type: Application
    Filed: February 13, 2023
    Publication date: September 28, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: SHRUTI KUNAL KUNDE, AMEY SANJAYKUMAR PANDIT, REKHA SINGHAL, SHAROD ROY CHOUDHURY
  • Publication number: 20230185778
    Abstract: The present disclosure provides a scalable acceleration of data processing in Machine Learning pipeline which is unavailable in conventional methods. Initially, the system receives a dataset and a data processing code. A plurality of sample datasets are obtained based on the received dataset using a sampling technique. A plurality of performance parameters corresponding to each of the plurality of sample datasets are obtained based on the data processing code using a profiling technique. A plurality of scalable performance parameters corresponding to each of a plurality of larger datasets are predicted based on the plurality of performance parameters and the data processing code using a curve fitting technique. Simultaneously, a plurality of anti-patterns are located in the data processing code using a pattern matching technique.
    Type: Application
    Filed: October 25, 2022
    Publication date: June 15, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: MAYANK MISHRA, ARCHISMAN BHOWMICK, REKHA SINGHAL
  • Publication number: 20230185625
    Abstract: Recent techniques for workload characterization of an application to be executed in a serverless execution environment or cloud are based on benchmark-approximation. Multiple microbenchmarks are run against the multiple VM configurations and a score is calculated which is used for mapping futuristic workloads to the appropriate configuration. Embodiments herein disclose method and system for workload characterization-based capacity planning of an actual application running on-premise with different configurations of the same machine and providing a cost-effective and high-performance serverless execution environment. Resource demand of each API in the application workflow is evaluated. Based on the resource demand of each API, a mapping is performed to the serverless platform on cloud. Additionally, characterization of threads within each API is performed and each thread is mapped to a serverless instance based on its resource requirements.
    Type: Application
    Filed: December 5, 2022
    Publication date: June 15, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: DHEERAJ CHAHAL, REKHA SINGHAL, Surya Chaitanya VENKATA PALEPU
  • Patent number: 11640542
    Abstract: The disclosure generally relates to system architectures, and, more particularly, to a method and system for system architecture recommendation. In existing scenario, a solution architect often gets minimum details about requirements, hence struggles to design a system architecture that matches the requirements. The method and system disclosed herein are to provide system recommendation in response to requirements provided as input to the system. The system generates an acyclic dependency graph based on parameters and values extracted from an obtained user input. The system then identifies a reference architectures that matches the requirements, and further selects components that match the architecture requirements. The system further selects technologies considering inter-operability of the technologies. Further, the system generates architecture recommendations for the user, based on the selected components, and technologies.
    Type: Grant
    Filed: March 20, 2019
    Date of Patent: May 2, 2023
    Assignee: Tata Consultancy Limited Services
    Inventors: Shruti Kunde, Chetan Phalak, Rekha Singhal, Manoj Nambiar
  • Patent number: 11488032
    Abstract: Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.
    Type: Grant
    Filed: March 22, 2019
    Date of Patent: November 1, 2022
    Assignee: Tata Consultancy Limited Services
    Inventors: Rekha Singhal, Gautam Shroff, Vartika Tewari, Sanket Kadarkar, Siddharth Verma, Sharod Roy Choudhury, Lovekesh Vig, Rupinder Virk
  • Patent number: 11449413
    Abstract: This disclosure relates generally to accelerating development and deployment of enterprise applications where the applications involve both data driven and task driven components in data driven enterprise information technology (IT) systems. The disclosed system is capable of determining components of the application that may be task-driven and/or those components which may be data-driven using inputs such as business use case, data sources and requirements specifications. The system is capable of determining the components that may be developed using task-driven and data-drive paradigms and enables migration of components from the task driven paradigm to the data driven paradigm. Also, the system trains a reinforcement learning (RL) model for facilitating migration of the identified components from the task driven paradigm to the data driven paradigm. The system is further capable of integrating the migrated and existing components to accelerate development and deployment an integrated IT application.
    Type: Grant
    Filed: June 11, 2021
    Date of Patent: September 20, 2022
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Rekha Singhal, Gautam Shroff, Dheeraj Chahal, Mayank Mishra, Shruti Kunde, Manoj Nambiar
  • Publication number: 20220214864
    Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.
    Type: Application
    Filed: September 2, 2021
    Publication date: July 7, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: REKHA SINGHAL, MAYANK MISHRA, DHEERAJ CHAHAL, SHRUTI KUNDE, MANJU RAMESH
  • Publication number: 20220092354
    Abstract: This disclosure relates generally to a method and system for generating labelled dataset using a training data recommender technique. Recommender systems face major challenges in handling dynamic data on machine learning paradigms thereby rendering inaccurate unlabeled dataset. The method of the present disclosure is based on a training data recommender technique suitably constructed with a newly defined parameter such as the labelled data prediction threshold to determine the adequate amount of labelled training data required for training the one or more machine learning models. The method processes the received unlabeled dataset for labelling the unlabeled dataset based on a labelled data prediction threshold which is determined using a trained training data recommender technique.
    Type: Application
    Filed: September 10, 2021
    Publication date: March 24, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Shruti Kunde, Mayank Mishra, Rekha Singhal, Amey Pandit, Manoj Nambiar, Gautam Shroff
  • Patent number: 11249876
    Abstract: A system and method for estimating execution time of an application with Spark™ platform in a production environment. The application on Spark™ platform is executed as a sequence of Spark jobs. Each Spark job is executed as a directed acyclic graph (DAG) consisting of stages. Each stage has multiple executors running in parallel and the each executor has set of concurrent tasks. Each executor spawns multiple threads, one for each task. All jobs in the same executor share the same JVM memory. The execution time for each Spark job is predicted as summation of the estimated execution time of all its stages. The execution time constitutes scheduler delay, serialization time, de-serialization time, and JVM overheads. The JVM time estimation depends on type of computation hardware system and number of threads.
    Type: Grant
    Filed: August 21, 2018
    Date of Patent: February 15, 2022
    Assignee: Tata Consultancy Services Limited
    Inventors: Rekha Singhal, Praveen Kumar Singh