Patents by Inventor Yogesh Laxmikant Chobe

Yogesh Laxmikant Chobe 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).

  • MACHINE LEARNING NETWORK IMPLEMENTED BY STATICALLY SCHEDULED INSTRUCTIONS
    Publication number: 20230023303
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Application
    Filed: October 3, 2022
    Publication date: January 26, 2023
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S.J Attia, Spenser Don Gilliland, Bradley Taylor
  • Machine learning network implemented by statically scheduled instructions, with system-on-chip
    Patent number: 11403519
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Grant
    Filed: April 6, 2020
    Date of Patent: August 2, 2022
    Assignee: SiMa Technologies, Inc.
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S. J Attia, Spenser Don Gilliland
  • Machine learning network implemented by statically scheduled instructions, with MLA chip
    Patent number: 11354570
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Grant
    Filed: April 6, 2020
    Date of Patent: June 7, 2022
    Assignee: SiMa Technologies, Inc.
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S. J Attia, Spenser Don Gilliland
  • Machine learning network implemented by statically scheduled instructions, with compiler
    Patent number: 11321607
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Grant
    Filed: April 3, 2020
    Date of Patent: May 3, 2022
    Assignee: SiMa Technologies, Inc.
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S. J Attia, Spenser Don Gilliland
  • MACHINE LEARNING NETWORK IMPLEMENTED BY STATICALLY SCHEDULED INSTRUCTIONS, WITH COMPILER
    Publication number: 20210312320
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Application
    Filed: April 3, 2020
    Publication date: October 7, 2021
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S.J Attia, Spenser Don Gilliland
  • MACHINE LEARNING NETWORK IMPLEMENTED BY STATICALLY SCHEDULED INSTRUCTIONS, WITH SYSTEM-ON-CHIP
    Publication number: 20210312322
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Application
    Filed: April 6, 2020
    Publication date: October 7, 2021
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S.J Attia, Spenser Don Gilliland
  • MACHINE LEARNING NETWORK IMPLEMENTED BY STATICALLY SCHEDULED INSTRUCTIONS, WITH MLA CHIP
    Publication number: 20210312267
    Abstract: A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.
    Type: Application
    Filed: April 6, 2020
    Publication date: October 7, 2021
    Inventors: Nishit Shah, Reed Kotler, Srivathsa Dhruvanarayan, Moenes Zaher Iskarous, Kavitha Prasad, Yogesh Laxmikant Chobe, Sedny S.J Attia, Spenser Don Gilliland