PATENT CLAIM ANALYSIS

Application Number: 15924029
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-07
Patent Classification: ["345", "506000"]

Abstract:
A method for optimization of machine learning (ML) workloads on a graphics processor unit (GPU). The method includes identifying a computation having a generic pattern commonly observed in ML processes. Hierarchical aggregation spanning a memory hierarchy of the GPU for processing is performed for the identified computation including maintaining partial output vector results in shared memory of the GPU. Hierarchical aggregation for vectors is performed including performing intra-block aggregation for multiple thread blocks of a partial output vector results on GPU global memory.

Claim (Index 16):
The computer program product of  claim 15 , wherein the unrolling comprises each thread scaling elements of the input vector, and aggregating the scaled elements into a local register as a partial result of the output vector, and once all assigned rows are processed, threads within each vector propagate their partial results in the local register to the global memory of the GPU.

Metadata:
- Claim Count in Document: 43.0
- Percentile: 90.0
- Lexical Diversity: 1.71698
- Patent Class: 345.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14813522', '15190073', '14580110', '14927428', '12569942']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6431705595215778
- 35 USC 102 Novelty (BERT): 0.5347203858365546
- Combined Prediction Score: 0.6323255421530755
- Mean Citation Score: 209.00097800000003
- Max Citation Score: 368.76996
- Similarity Product: 364.4466126578449

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test