Yang Wu Phones & Addresses
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43 Bismark St, Daly City, CA 94014
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33 Minerva St, San Francisco, CA 94112 (415) 586-8627
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Castro Valley, CA
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San Mateo, CA
Business Records
Name / Title
Company / Classification
Phones & Addresses
Principal
Poetry Times Inc
Business Services at Non-Commercial Site
Business Services at Non-Commercial Site
35939 Killorglin Cmn, Fremont, CA 94536
Publications
Us Patents
Glitch Power Analysis And Optimization Engine
View pageUS Patent:
20210384901, Dec 9, 2021
Filed:
Jun 29, 2020
Appl. No.:
16/915099
Inventors:
- Plano TX, US
Ping-San Tzeng - Fremont CA, US
Chao-Yung Wang - Taipei, TW
Yang Wu - Los Altos Hills CA, US
Wen Kung Chu - San Jose CA, US
Ping-San Tzeng - Fremont CA, US
Chao-Yung Wang - Taipei, TW
Yang Wu - Los Altos Hills CA, US
Wen Kung Chu - San Jose CA, US
International Classification:
H03K 17/687
G01R 31/317
G01R 31/317
Abstract:
A switching activity report of simulated switching activities of a semiconductor circuit is accessed. A plurality of glitch bottleneck ratios corresponding to a plurality of pins in the semiconductor circuit are determined, comprising by: setting an initial bottleneck ratio on a leaf output pin; and backward traversing the semiconductor circuit to determine a plurality of glitch bottleneck ratios of pins in a fan-in cone of the leaf output pin.A plurality of total glitch powers associated with the plurality of pins is determined, a total glitch power of the plurality of total glitch powers being determined based on a glitch bottleneck ratio and a glitch power of a corresponding pin. One or more critical bottleneck pins among the plurality of pins are identified based on the plurality of total glitch powers. One or more gates associated with the one or more critical bottleneck pins are adjusted to reduce corresponding one or more total glitch powers of the one or more gates.
Systems And Methods For Integrated Multi-Factor Multi-Label Analysis
View pageUS Patent:
20200356803, Nov 12, 2020
Filed:
May 10, 2019
Appl. No.:
16/409185
Inventors:
- San Jose CA, US
Nitin Sharma - San Jose CA, US
Fransisco Kurniadi - Dublin CA, US
Yang Wu - Milpitas CA, US
Nitin Sharma - San Jose CA, US
Fransisco Kurniadi - Dublin CA, US
Yang Wu - Milpitas CA, US
International Classification:
G06K 9/62
G06Q 30/02
G06N 3/04
G06N 3/08
G06Q 30/02
G06N 3/04
G06N 3/08
Abstract:
Systems and methods for integrated multi-factor multi-label analysis include using one or more deep learning systems, such as neural networks, to analyze how well one or more entities are likely to benefit from a targeted action. Data associated with each of the entities is analyzed to determine a score for each of the proposed targeted actions using multiple analysis factors. The scores for each analysis factor are determined using a different multi-layer analysis network for each analysis factor. The scores for each analysis factor are then combined to determine an overall score for each of the proposed targeted actions. The entities and the proposed targeted actions with the highest scores are then identified and then used to determine which entities are to be the subject of which targeted actions.