KR102110486B1 - 인공 뉴럴 네트워크 클래스-기반 프루닝 - Google Patents

인공 뉴럴 네트워크 클래스-기반 프루닝 Download PDF

Info

Publication number
KR102110486B1
KR102110486B1 KR1020170173396A KR20170173396A KR102110486B1 KR 102110486 B1 KR102110486 B1 KR 102110486B1 KR 1020170173396 A KR1020170173396 A KR 1020170173396A KR 20170173396 A KR20170173396 A KR 20170173396A KR 102110486 B1 KR102110486 B1 KR 102110486B1
Authority
KR
South Korea
Prior art keywords
neural network
artificial neural
neurons
activation frequency
object classes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
KR1020170173396A
Other languages
English (en)
Korean (ko)
Other versions
KR20180072562A (ko
Inventor
지안단 첸
로빈 세이볼드
한나 브죄르그빈스도트티르
마틴 엘준그뷰비스트
Original Assignee
엑시스 에이비
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 엑시스 에이비 filed Critical 엑시스 에이비
Publication of KR20180072562A publication Critical patent/KR20180072562A/ko
Application granted granted Critical
Publication of KR102110486B1 publication Critical patent/KR102110486B1/ko
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G06N3/0445—
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/40—Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/061—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using biological neurons, e.g. biological neurons connected to an integrated circuit
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Neurology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Multimedia (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Human Computer Interaction (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Image Analysis (AREA)
KR1020170173396A 2016-12-21 2017-12-15 인공 뉴럴 네트워크 클래스-기반 프루닝 Active KR102110486B1 (ko)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP16205831.7A EP3340129B1 (en) 2016-12-21 2016-12-21 Artificial neural network class-based pruning
EP16205831.7 2016-12-21

Publications (2)

Publication Number Publication Date
KR20180072562A KR20180072562A (ko) 2018-06-29
KR102110486B1 true KR102110486B1 (ko) 2020-05-13

Family

ID=57583087

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020170173396A Active KR102110486B1 (ko) 2016-12-21 2017-12-15 인공 뉴럴 네트워크 클래스-기반 프루닝

Country Status (6)

Country Link
US (1) US10552737B2 (enExample)
EP (1) EP3340129B1 (enExample)
JP (1) JP6755849B2 (enExample)
KR (1) KR102110486B1 (enExample)
CN (1) CN108229667B (enExample)
TW (1) TWI698807B (enExample)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024043771A1 (ko) * 2022-08-24 2024-02-29 동국대학교 산학협력단 사이버 공격에 강인한 인공 신경망 구조 재구성 장치 및 방법

Families Citing this family (31)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108268947A (zh) * 2016-12-30 2018-07-10 富士通株式会社 用于提高神经网络的处理速度的装置和方法及其应用
US10892050B2 (en) * 2018-04-13 2021-01-12 International Business Machines Corporation Deep image classification of medical images
KR101989793B1 (ko) 2018-10-22 2019-06-17 한국기술교육대학교 산학협력단 컨볼루션 신경망을 위한 가속기 인식 가지 치기 방법 및 기록 매체
KR102796861B1 (ko) 2018-12-10 2025-04-17 삼성전자주식회사 인공 신경망을 압축하기 위한 장치 및 방법
KR102865734B1 (ko) * 2019-01-02 2025-09-26 삼성전자주식회사 뉴럴 네트워크 최적화 장치 및 뉴럴 네트워크 최적화 방법
US11551093B2 (en) * 2019-01-22 2023-01-10 Adobe Inc. Resource-aware training for neural networks
KR102214837B1 (ko) * 2019-01-29 2021-02-10 주식회사 디퍼아이 컨벌루션 신경망 파라미터 최적화 방법, 컨벌루션 신경망 연산방법 및 그 장치
JP7099968B2 (ja) * 2019-01-31 2022-07-12 日立Astemo株式会社 演算装置
JP7225876B2 (ja) 2019-02-08 2023-02-21 富士通株式会社 情報処理装置、演算処理装置および情報処理装置の制御方法
DE102019202816A1 (de) * 2019-03-01 2020-09-03 Robert Bosch Gmbh Training neuronaler Netzwerke für effizientes Implementieren auf Hardware
KR102368962B1 (ko) 2019-03-22 2022-03-03 국민대학교산학협력단 멤리스터 어레이 회로를 제어하기 위한 게이트 회로를 포함하는 뉴럴 네트워크 시스템
JP7150651B2 (ja) * 2019-03-22 2022-10-11 株式会社日立ソリューションズ・テクノロジー ニューラルネットワークのモデル縮約装置
JP2022095999A (ja) * 2019-04-19 2022-06-29 国立大学法人北海道大学 ニューラル計算装置、および、ニューラル計算方法
KR102782971B1 (ko) 2019-05-08 2025-03-18 삼성전자주식회사 인공 신경망 모델을 트레이닝하는 컴퓨팅 장치, 인공 신경망 모델을 트레이닝하는 방법 및 이를 저장하는 메모리 시스템
JP6787444B1 (ja) 2019-05-23 2020-11-18 沖電気工業株式会社 ニューラルネットワーク軽量化装置、ニューラルネットワーク軽量化方法およびプログラム
CN112070221B (zh) * 2019-05-31 2024-01-16 中科寒武纪科技股份有限公司 运算方法、装置及相关产品
US11514311B2 (en) * 2019-07-03 2022-11-29 International Business Machines Corporation Automated data slicing based on an artificial neural network
US12141699B2 (en) 2019-08-29 2024-11-12 Alibaba Group Holding Limited Systems and methods for providing vector-wise sparsity in a neural network
JP7111671B2 (ja) * 2019-09-05 2022-08-02 株式会社東芝 学習装置、学習システム、および学習方法
DE102019128715A1 (de) * 2019-10-24 2021-04-29 Krohne Messtechnik Gmbh Verfahren zur Erzeugung eines neuronalen Netzes für ein Feldgerät zur Vorhersage von Feldgerätfehlern und ein entsprechendes System
US11816574B2 (en) * 2019-10-25 2023-11-14 Alibaba Group Holding Limited Structured pruning for machine learning model
JP7396117B2 (ja) * 2020-02-27 2023-12-12 オムロン株式会社 モデル更新装置、方法、及びプログラム
JP7495833B2 (ja) * 2020-07-07 2024-06-05 株式会社日立ソリューションズ・テクノロジー Ｄｎｎモデル圧縮システム
EP3945470A1 (en) * 2020-07-31 2022-02-02 Aptiv Technologies Limited Methods and systems for reducing the complexity of a computational network
KR20220048832A (ko) 2020-10-13 2022-04-20 삼성전자주식회사 인공 신경망 프루닝 방법 및 장치
US12340313B2 (en) 2020-11-30 2025-06-24 Moffett International Co., Limited Neural network pruning method and system via layerwise analysis
CN115393662A (zh) * 2021-05-08 2022-11-25 Oppo广东移动通信有限公司 图像处理方法、装置、计算机设备及存储介质
JP7700650B2 (ja) * 2021-11-25 2025-07-01 富士通株式会社 モデル削減プログラム、装置、及び方法
GB202118066D0 (en) 2021-12-14 2022-01-26 Univ Dublin Class separation aware artificial neural network pruning method
US12061632B2 (en) * 2022-03-29 2024-08-13 Treasure Data, Inc. Interactive adaptation of machine learning models for time series data
US20250190694A1 (en) * 2023-12-07 2025-06-12 International Business Machines Corporation Limiting undesired large language model (llm) output

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140180986A1 (en) 2012-12-24 2014-06-26 Google Inc. System and method for addressing overfitting in a neural network

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5787408A (en) * 1996-08-23 1998-07-28 The United States Of America As Represented By The Secretary Of The Navy System and method for determining node functionality in artificial neural networks
JP2000298661A (ja) * 1999-04-15 2000-10-24 Fuji Xerox Co Ltd ニューラルネットワーク装置
US7308418B2 (en) * 2004-05-24 2007-12-11 Affinova, Inc. Determining design preferences of a group
JP4175296B2 (ja) * 2004-06-25 2008-11-05 キャタピラージャパン株式会社 建設機械のデータ処理装置及び建設機械のデータ処理方法
EP2533176A1 (en) * 2005-11-15 2012-12-12 Bernadette Garner Method for determining whether input vectors are known or unknown by a neuron
US7800490B2 (en) * 2008-01-09 2010-09-21 Sensormatic Electronics, LLC Electronic article surveillance system neural network minimizing false alarms and failures to deactivate
WO2012055045A2 (en) * 2010-10-27 2012-05-03 Solido Design Automation Inc. Method and system for identifying rare-event failure rates
US9147155B2 (en) * 2011-08-16 2015-09-29 Qualcomm Incorporated Method and apparatus for neural temporal coding, learning and recognition
KR101359352B1 (ko) * 2012-06-28 2014-02-21 한국과학기술원 시각신경 회로장치 및 이를 이용한 시각신경 모방 시스템
CN103209417B (zh) * 2013-03-05 2016-01-20 北京邮电大学 基于神经网络的频谱占用状态的预测方法以及装置
US9747554B2 (en) * 2013-05-24 2017-08-29 Qualcomm Incorporated Learning device with continuous configuration capability
US9721204B2 (en) * 2013-10-28 2017-08-01 Qualcomm Incorporated Evaluation of a system including separable sub-systems over a multidimensional range
US10373054B2 (en) * 2015-04-19 2019-08-06 International Business Machines Corporation Annealed dropout training of neural networks
CN107563497B (zh) * 2016-01-20 2021-03-19 中科寒武纪科技股份有限公司 用于稀疏人工神经网络的计算装置和运算方法
US11315018B2 (en) * 2016-10-21 2022-04-26 Nvidia Corporation Systems and methods for pruning neural networks for resource efficient inference

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140180986A1 (en) 2012-12-24 2014-06-26 Google Inc. System and method for addressing overfitting in a neural network

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024043771A1 (ko) * 2022-08-24 2024-02-29 동국대학교 산학협력단 사이버 공격에 강인한 인공 신경망 구조 재구성 장치 및 방법

Also Published As

Publication number Publication date
CN108229667B (zh) 2019-09-10
TWI698807B (zh) 2020-07-11
CN108229667A (zh) 2018-06-29
TW201824093A (zh) 2018-07-01
JP2018129033A (ja) 2018-08-16
US20180181867A1 (en) 2018-06-28
JP6755849B2 (ja) 2020-09-16
EP3340129B1 (en) 2019-01-30
KR20180072562A (ko) 2018-06-29
US10552737B2 (en) 2020-02-04
EP3340129A1 (en) 2018-06-27

Similar Documents

Publication Publication Date Title
KR102110486B1 (ko) 인공 뉴럴 네트워크 클래스-기반 프루닝
EP3891662B1 (en) Automated generation of machine learning models
CN112508186B (zh) 训练用于图像识别的神经网络的方法和神经网络设备
CN111598238A (zh) 深度学习模型的压缩方法及装置
KR102788176B1 (ko) 뉴럴 네트워크 학습 방법 및 그 시스템
US20220076077A1 (en) Quality estimation model trained on training signals exhibiting diverse impairments
US20190138929A1 (en) System and method for automatic building of learning machines using learning machines
CN113011532A (zh) 分类模型训练方法、装置、计算设备及存储介质
CN113392867A (zh) 一种图像识别方法、装置、计算机设备及存储介质
CN119314020B (zh) 基于脉冲神经网络的视觉识别方法及装置
CA3143928A1 (en) Dynamic image resolution assessment
KR20210111677A (ko) 뉴럴 네트워크의 클리핑 방법, 뉴럴 네트워크의 컨벌루션 계산 방법 및 그 방법을 수행하기 위한 전자 장치
US20240078432A1 (en) Self-tuning model compression methodology for reconfiguring deep neural network and electronic device
JP7507172B2 (ja) 情報処理方法、情報処理システム及び情報処理装置
CN113139466B (zh) 基于单隐层神经网络的图像识别方法及相关设备
CN112598020B (zh) 目标识别方法及系统
US12468979B2 (en) Early stopping of artificial intelligence model training using control limits
CN110717578A (zh) 神经网络压缩方法、图像处理方法及装置
CN115222012A (zh) 用于压缩神经网络的方法和装置
US20220405561A1 (en) Electronic device and controlling method of electronic device
CN116126815A (zh) 基于剪枝和量化训练的自动驾驶模型压缩方法及装置
JPWO2023032438A5 (enExample)
CN120564761B (zh) 一种基于长短期记忆网络优化的变压器故障声纹检测方法
JP7055211B2 (ja) データ処理システムおよびデータ処理方法
CN119106714A (zh) 用于联机手写字符识别模型的量化方法、装置及存储介质

Legal Events

Date Code Title Description
PA0109 Patent application

Patent event code: PA01091R01D

Comment text: Patent Application

Patent event date: 20171215

PG1501 Laying open of application
A201 Request for examination
A302 Request for accelerated examination
PA0201 Request for examination

Patent event code: PA02012R01D

Patent event date: 20200114

Comment text: Request for Examination of Application

Patent event code: PA02011R01I

Patent event date: 20171215

Comment text: Patent Application

PA0302 Request for accelerated examination

Patent event date: 20200114

Patent event code: PA03022R01D

Comment text: Request for Accelerated Examination

Patent event date: 20171215

Patent event code: PA03021R01I

Comment text: Patent Application

E701 Decision to grant or registration of patent right
PE0701 Decision of registration

Patent event code: PE07011S01D

Comment text: Decision to Grant Registration

Patent event date: 20200312

GRNT Written decision to grant
PR0701 Registration of establishment

Comment text: Registration of Establishment

Patent event date: 20200507

Patent event code: PR07011E01D

PR1002 Payment of registration fee

Payment date: 20200508

End annual number: 3

Start annual number: 1

PG1601 Publication of registration
PR1001 Payment of annual fee

Payment date: 20240424

Start annual number: 5

End annual number: 5