Pune
India
65
2026-04-30
The entities that hold a legal rights for patent applications filed by inventor Das Dipankar:
Dipankar Das from Pune, IN has applied for patents for these inventions. The list has both pending applications and granted patents:
COMMUNICATION OPTIMIZATIONS FOR DISTRIBUTED MACHINE LEARNING
#2 | 2026-04-02UTILIZING STRUCTURED SPARSITY IN SYSTOLIC ARRAYS
#3 | 2026-01-08DYNAMIC PRECISION MANAGEMENT FOR INTEGER DEEP LEARNING PRIMITIVES
#4 | 2026-01-08SYSTOLIC ARRAY HAVING SUPPORT FOR OUTPUT SPARSITY
#5 | 2026-01-01ABSTRACTION LAYERS FOR SCALABLE DISTRIBUTED MACHINE LEARNING
#6 | 2025-11-27HARDWARE IMPLEMENTED POINT TO POINT COMMUNICATION PRIMITIVES FOR MACHINE LEARNING
#7 | 2025-11-27OPTIMIZED COMPUTE HARDWARE FOR MACHINE LEARNING OPERATIONS
#8 | 2025-07-03INSTRUCTIONS FOR FUSED MULTIPLY-ADD OPERATIONS WITH VARIABLE PRECISION INPUT OPERANDS
#9 | 2025-06-19DATA PARALLELISM AND HALO EXCHANGE FOR DISTRIBUTED MACHINE LEARNING
#10 | 2025-05-29INCREMENTAL PRECISION NETWORKS USING RESIDUAL INFERENCE AND FINE-GRAIN QUANTIZATION
#11 | 2025-02-20SCALING HALF-PRECISION FLOATING POINT TENSORS FOR TRAINING DEEP NEURAL NETWORKS
#12 | 2024-12-12DYNAMIC PRECISION MANAGEMENT FOR INTEGER DEEP LEARNING PRIMITIVES
#13 | 2024-09-26UTILIZING STRUCTURED SPARSITY IN SYSTOLIC ARRAYS
#14 | 2024-05-16Incremental precision networks using residual inference and fine-grain quantization
#15 | 2024-04-18Instructions for fused multiply-add operations with variable precision input operands
#16 | 2024-04-11APPARATUSES, METHODS, AND SYSTEMS FOR NEURAL NETWORKS
#17 | 2024-02-29ABSTRACTION LAYERS FOR SCALABLE DISTRIBUTED MACHINE LEARNING
#18 | 2023-12-21Scaling half-precision floating point tensors for training deep neural networks
#19 | 2023-11-23COMMUNICATION OPTIMIZATIONS FOR DISTRIBUTED MACHINE LEARNING
#20 | 2023-11-02Dynamic precision management for integer deep learning primitives
#21 | 2023-06-08HARDWARE IMPLEMENTED POINT TO POINT COMMUNICATION PRIMITIVES FOR MACHINE LEARNING
#22 | 2023-05-11Scaling half-precision floating point tensors for training deep neural networks
#23 | 2023-03-23Incremental precision networks using residual inference and fine-grain quantization
#24 | 2022-12-29SYSTOLIC ARRAY HAVING SUPPORT FOR OUTPUT SPARSITY
#25 | 2022-11-17Data parallelism and halo exchange for distributed machine learning
#26 | 2022-10-27Optimized compute hardware for machine learning operations
#27 | 2022-10-13Dynamic precision management for integer deep learning primitives
#28 | 2022-10-13Instructions and logic for vector multiply add with zero skipping
#29 | 2022-08-25Scaling half-precision floating point tensors for training deep neural networks
#30 | 2022-08-04Communication optimizations for distributed machine learning
#31 | 2022-07-07Instructions for fused multiply-add operations with variable precision input operands
#32 | 2022-04-14SCALABLE HIGH-PERFORMANCE PACKAGE ARCHITECTURE USING PROCESSOR-MEMORY-PHOTONICS MODULES
#33 | 2022-03-31Abstraction layers for scalable distributed machine learning
#34 | 2022-02-17APPARATUSES, METHODS, AND SYSTEMS FOR NEURAL NETWORKS
#35 | 2021-12-09Apparatuses, methods, and systems for access synchronization in a shared memory
#36 | 2021-11-11ABSTRACTION LIBRARY TO ENABLE SCALABLE DISTRIBUTED MACHINE LEARNING
#37 | 2021-11-04TECHNOLOGIES FOR SCALING DEEP LEARNING TRAINING
#38 | 2021-06-24Instructions and logic for vector multiply add with zero skipping
#39 | 2021-04-15Dynamic precision management for integer deep learning primitives
#40 | 2021-04-15PARALLEL PROCESSING BASED ON INJECTION NODE BANDWIDTH
#41 | 2021-03-18Utilizing structured sparsity in systolic arrays
#42 | 2021-03-11Conversion hardware mechanism
#43 | 2021-01-21Optimized compute hardware for machine learning operations
#44 | 2020-08-20Dynamic precision management for integer deep learning primitives
#45 | 2020-08-13Instructions for fused multiply-add operations with variable precision input operands
#46 | 2019-11-21Scaling half-precision floating point tensors for training deep neural networks
#47 | 2019-10-03APPARATUSES, METHODS, AND SYSTEMS FOR NEURAL NETWORKS
#48 | 2019-08-08Apparatuses, methods, and systems for access synchronization in a shared memory
#49 | 2019-07-25Apparatus and method for vector multiply and accumulate of packed words
#50 | 2019-07-04Communication optimizations for distributed machine learning
#51 | 2019-02-07Circuitry for low-precision deep learning
#52 | 2019-02-07Instructions for fused multiply-add operations with variable precision input operands
#53 | 2019-02-07Apparatus and method for vector multiply and accumulate of packed bytes
#54 | 2018-11-08Dynamic precision management for integer deep learning primitives
#55 | 2018-11-08Data parallelism and halo exchange for distributed machine learning
#56 | 2018-11-08Optimized compute hardware for machine learning operations
#57 | 2018-11-08Hardware implemented point to point communication primitives for machine learning
#58 | 2018-11-08Scaling half-precision floating point tensors for training deep neural networks
#59 | 2018-11-01Incremental precision networks using residual inference and fine-grain quantization
#60 | 2018-10-11Abstraction layers for scalable distributed machine learning
#61 | 2018-10-11Abstraction library to enable scalable distributed machine learning
#62 | 2018-10-04Technologies for scaling deep learning training
#63 | 2014-01-16Memory management systems and methods for embedded systems
#64 | 2013-05-16Recovering from stack corruption faults in embedded software systems
#65 | 2011-10-06Method and apparatus for operational-level functional and degradation fault analysis
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