PATENT CLAIM ANALYSIS

Application Number: 16321097
Application Type: Utility
Filing Date: 2019-01
Publication Date: 2019-05
Patent Classification: ["706", "027000"]

Abstract:
Aspects disclosed in the detailed description include memory compression in a deep neural network (DNN). To support a DNN application, a fully connected weight matrix associated with a hidden layer(s) of the DNN is divided into a plurality of weight blocks to generate a weight block matrix with a first number of rows and a second number of columns. A selected number of weight blocks are randomly designated as active weight blocks in each of the first number of rows and updated exclusively during DNN training. The weight block matrix is compressed to generate a sparsified weight block matrix including exclusively active weight blocks. The second number of columns is compressed to reduce memory footprint and computation power, while the first number of rows is retained to maintain accuracy of the DNN, thus providing the DNN in an efficient hardware implementation without sacrificing accuracy of the DNN application.

Claim (Index 3):
The method of  claim 1  further comprising:\n generating a binary connection coefficient identifying each non-zero weight and each zero weight in each of the selected number of active weight blocks in each of the first number of rows; and \n updating exclusively the non-zero weight identified by the binary connection coefficient during the DNN training.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 99.0
- Lexical Diversity: 2.09091
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13305741', '13920323', '13787821', '13786470', '14699778']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3712871085053801
- 35 USC 102 Novelty (BERT): 0.501213798647173
- Combined Prediction Score: 0.3842797775195595
- Mean Citation Score: 240.035722
- Max Citation Score: 261.70746
- Similarity Product: 180.5678670480967

Labels:
- Claim Label 101: 1
- Claim Label 102: 0
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 1

Dataset: test