CN113408226B9 - 一种基于深度学习的芯片供电网络凸块电流估算方法及系统 - Google Patents
一种基于深度学习的芯片供电网络凸块电流估算方法及系统 Download PDFInfo
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- CN113408226B9 CN113408226B9 CN202110505699.7A CN202110505699A CN113408226B9 CN 113408226 B9 CN113408226 B9 CN 113408226B9 CN 202110505699 A CN202110505699 A CN 202110505699A CN 113408226 B9 CN113408226 B9 CN 113408226B9
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- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000013135 deep learning Methods 0.000 title claims abstract description 19
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- 238000013528 artificial neural network Methods 0.000 claims description 24
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- 238000004088 simulation Methods 0.000 claims description 18
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/32—Circuit design at the digital level
- G06F30/33—Design verification, e.g. functional simulation or model checking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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CN113408226A CN113408226A (zh) | 2021-09-17 |
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TWI625681B (zh) * | 2017-05-11 | 2018-06-01 | 國立交通大學 | 神經網路處理系統 |
CN108090658A (zh) * | 2017-12-06 | 2018-05-29 | 河北工业大学 | 基于时域特征参数融合的电弧故障诊断方法 |
CN109598768B (zh) * | 2018-10-11 | 2023-03-28 | 天津大学 | 基于卷积神经网络的电学层析成像图像重建方法 |
CN112348159A (zh) * | 2019-08-07 | 2021-02-09 | 青岛鼎信通讯股份有限公司 | 一种故障电弧神经网络优化训练方法 |
KR102163828B1 (ko) * | 2019-09-24 | 2020-10-12 | 한국생산기술연구원 | 용접 파형의 스패터 예측을 위한 머신 러닝 시스템 및 방법 |
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CN113408226A (zh) | 2021-09-17 |
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Correction item: Denomination of Invention|Abstract|Claims|Description Correct: A Deep Learning Based Method and System for Estimating Convex Current in Chip Power Supply Networks|correct False: A Method and System for Estimating Convex Fast Current in Chip Power Supply Networks Based on Deep Learning|error Number: 12-02 Page: ?? Volume: 39 Correction item: Denomination of Invention Correct: A Deep Learning Based Method and System for Estimating Convex Current in Chip Power Supply Networks False: A Method and System for Estimating Convex Fast Current in Chip Power Supply Networks Based on Deep Learning Number: 12-02 Volume: 39 |
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