For the Inventor, By the Inventor

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San Jose, CA, United States of America

Sima Technologies, Inc.

USPTO Granted Patents = 23

% Expired Patents = 4.3

EPO Patents = 1

Trademarks = 6

Forward Citations = 33

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18 inventors (with patents filed for the assignee):

Nishit Harshad Shah (13 out of 24 patents)

Reed Kotler (12 out of 12 patents)

Srivathsa Dhruvanarayan (9 out of 20 patents)

4. Moenes Zaher Iskarous (5 out of 6 patents)

5. Kavitha Prasad (4 out of 13 patents)

6. Yogesh Laxmikant Chobe (4 out of 6 patents)

7. Sedny S J Attia (4 out of 4 patents)

8. Spenser Don Gilliland (4 out of 6 patents)

9. Subba Rao Venkata Kalari (3 out of 3 patents)

10. Saurabh Jain (3 out of 3 patents)

11. Bradley L Taylor (2 out of 22 patents)

12. Gwangho Kim (2 out of 2 patents)

13. Chia-Hsin Chen (1 out of 27 patents)

14. David Hochendoner (1 out of 9 patents)

15. Sounil Biswas (1 out of 4 patents)

23 patents: Sort by Forward Citations

1. 12717375 - Reconvergent clock mesh for on-chip compute cluster (2026-08-25),

2. 12664474 - Gap counters for synchronization of compute elements executing statically scheduled instructions for a machine learning accelerator (2026-06-23),

3. 12657444 - Machine learning network implemented by statically scheduled instructions (2026-06-16),

4. 12639118 - Managing current consumption in a machine learning accelerator (2026-05-26),

5. 12639056 - Adaptive search-based allocation of computations to synchronized, interconnected processing elements for implementing machine learning networks (2026-05-26),

6. 12399688 - Development platform for image processing pipelines that use machine learning with user interface (2025-08-26),

7. 12333351 - Synchronization of processing elements that execute statically scheduled instructions in a machine learning accelerator (2025-06-17),

8. 12260253 - Layout-based data transfer between synchronized, interconnected processing elements for implementing machine learning networks (2025-03-25),

9. 12067465 - Instruction streaming for a machine learning accelerator (2024-08-20),

10. 12026510 - Using cycle counts to serve compute elements executing statically scheduled instructions for a machine learning accelerator (2024-07-02),

11. 11989581 - Software managed memory hierarchy (2024-05-21),

12. 11886981 - Inter-processor data transfer in a machine learning accelerator, using statically scheduled instructions (2024-01-30),

13. 11803740 - Ordering computations of a machine learning network in a machine learning accelerator for efficient memory usage (2023-10-31),

14. 11782757 - Scheduling off-chip memory access for programs with predictable execution (2023-10-10),

15. 11734605 - Allocating computations of a machine learning network in a machine learning accelerator (2023-08-22),

Company Overview

Sima Technologies, Inc. is a technology firm based in San Jose, California, specializing in advanced engineering solutions and innovative software development. The company focuses on creating high-performance systems and applications that cater to various industries, including telecommunications, automotive, and aerospace. With a commitment to research and development, Sima Technologies aims to drive technological advancements and enhance operational efficiencies through its cutting-edge products and services.

Profile summary synthesized from public USPTO records and patent filings.

Company Badge

USPTO Data Jan 1 1976 - Oct 6 2026

23 Patents

#315 in San Jose, CA

#3,416 in California

#14,415 in US

#27,649 in the World

San Jose, CA, United States of America

Sima Technologies, Inc.

Forward Citations = 33

18 inventors:

Nishit Harshad Shah (13 out of 24 patents)

Reed Kotler (12 out of 12 patents)

Srivathsa Dhruvanarayan (9 out of 20 patents)

4. Moenes Zaher Iskarous (5 out of 6 patents)

5. Kavitha Prasad (4 out of 13 patents)

IDiyas Innovation Intelligence. (2026). Company Patent Portfolio: Sima Technologies, Inc.. Retrieved from https://idiyas.com/company/sima-technologies

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]

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