Bothell, Washington
United States
35
2026-01-15
The entities that hold a legal rights for patent applications filed by inventor Lo Daniel:
Daniel Lo from Bothell, US has applied for patents for these inventions. The list has both pending applications and granted patents:
COMPRESSION AND STORAGE OF NEURAL NETWORK ACTIVATIONS FOR BACKPROPAGATION
#2 | 2025-02-20ADJUSTING ACTIVATION COMPRESSION FOR NEURAL NETWORK TRAINING
#3 | 2024-08-15HIGH-PERFORMANCE MICROCODED TEXT PARSER
#4 | 2024-05-09Neural network activation compression with non-uniform mantissas
#5 | 2023-12-14PARALLELIZED DECODING OF VARIABLE-LENGTH PREFIX CODES
#6 | 2023-08-24TRAINING NEURAL NETWORK ACCELERATORS USING MIXED PRECISION DATA FORMATS
#7 | 2023-06-22RESIDUAL QUANTIZATION FOR NEURAL NETWORKS
#8 | 2023-05-04Neural network activation compression with non-uniform mantissas
#9 | 2023-04-13Neural network training with decreased memory consumption and processor utilization
#10 | 2022-09-15High-performance table-based state machine
#11 | 2022-03-17High-performance table-based state machine
#12 | 2022-03-17High-performance microcoded text parser
#13 | 2021-12-28Parallelized decoding of variable-length prefix codes
#14 | 2021-02-25Neural network training with decreased memory consumption and processor utilization
#15 | 2020-09-24Outlier quantization for training and inference
#16 | 2020-09-24MIXED PRECISION TRAINING OF AN ARTIFICIAL NEURAL NETWORK
#17 | 2020-09-03Deriving a concordant software neural network layer from a quantized firmware neural network layer
#18 | 2020-08-27Neural network layer processing with normalization and transformation of data
#19 | 2020-08-27Neural network layer processing with scaled quantization
#20 | 2020-08-20Adjusting activation compression for neural network training
#21 | 2020-07-30Neural network activation compression with non-uniform mantissas
#22 | 2020-07-09DITHERED QUANTIZATION OF PARAMETERS DURING TRAINING WITH A MACHINE LEARNING TOOL
#23 | 2020-07-02ADJUSTING PRECISION AND TOPOLOGY PARAMETERS FOR NEURAL NETWORK TRAINING BASED ON A PERFORMANCE METRIC
#24 | 2020-07-02Neural network activation compression with outlier block floating-point
#25 | 2020-07-02NEURAL NETWORK ACTIVATION COMPRESSION WITH NARROW BLOCK FLOATING-POINT
#26 | 2020-06-25SCALED LEARNING FOR TRAINING DNN
#27 | 2020-06-18Training neural network accelerators using mixed precision data formats
#28 | 2020-06-18Residual quantization for neural networks
#29 | 2019-11-14Training neural networks using mixed precision computations
#30 | 2019-11-14Block floating point computations using shared exponents
#31 | 2019-11-07QUANTIZATION FOR DNN ACCELERATORS
#32 | 2019-11-07Flow for quantized neural networks
#33 | 2019-11-07Block floating point computations using reduced bit-width vectors
#34 | 2019-09-19HARDWARE ACCELERATED NEURAL NETWORK SUBGRAPHS
#35 | 2019-01-01Hardware node having a matrix vector unit with block-floating point processing
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