GB2583623A - Fusing sparse kernels to approximate a full kernel of a convolutional neural network - Google Patents

Fusing sparse kernels to approximate a full kernel of a convolutional neural network Download PDF

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Publication number
GB2583623A
GB2583623A GB2010475.8A GB202010475A GB2583623A GB 2583623 A GB2583623 A GB 2583623A GB 202010475 A GB202010475 A GB 202010475A GB 2583623 A GB2583623 A GB 2583623A
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kernel
complementary
sparse
pattern
sample
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GB202010475D0 (en
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Chen Richard
Fan Quanfu
Pistoia Marco
Suzumura Toyotaro
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International Business Machines Corp
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International Business Machines Corp
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    • 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
    • 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
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2134—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
    • G06F18/21345—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis enforcing sparsity or involving a domain transformation
    • 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
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • 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
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0495—Quantised networks; Sparse networks; Compressed 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
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • 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

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Multimedia (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Image Analysis (AREA)
  • Complex Calculations (AREA)
GB2010475.8A 2017-12-14 2018-12-13 Fusing sparse kernels to approximate a full kernel of a convolutional neural network Withdrawn GB2583623A (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US15/841,480 US10740659B2 (en) 2017-12-14 2017-12-14 Fusing sparse kernels to approximate a full kernel of a convolutional neural network
PCT/IB2018/059993 WO2019116291A1 (en) 2017-12-14 2018-12-13 Fusing sparse kernels to approximate a full kernel of a convolutional neural network

Publications (2)

Publication Number Publication Date
GB202010475D0 GB202010475D0 (en) 2020-08-19
GB2583623A true GB2583623A (en) 2020-11-04

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GB2010475.8A Withdrawn GB2583623A (en) 2017-12-14 2018-12-13 Fusing sparse kernels to approximate a full kernel of a convolutional neural network

Country Status (6)

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US (1) US10740659B2 (enExample)
JP (1) JP7179850B2 (enExample)
CN (1) CN111344720A (enExample)
DE (1) DE112018006377T5 (enExample)
GB (1) GB2583623A (enExample)
WO (1) WO2019116291A1 (enExample)

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KR102548718B1 (ko) * 2017-06-07 2023-06-28 삼성전자주식회사 전자 장치 및 그 제어 방법
US12393829B2 (en) * 2019-07-25 2025-08-19 Samsung Electronics Co., Ltd. Methods and systems with convolutional neural network (CNN) performance
US11144290B2 (en) 2019-09-13 2021-10-12 Huawei Technologies Co., Ltd. Method and apparatus for enabling autonomous acceleration of dataflow AI applications
US12265911B2 (en) * 2020-02-06 2025-04-01 Google Llc Neural network layers with a controlled degree of spatial invariance
US20210256385A1 (en) * 2020-02-14 2021-08-19 Northeastern University Computer-implemented methods and systems for dnn weight pruning for real-time execution on mobile devices
US11494875B2 (en) 2020-03-25 2022-11-08 Nintendo Co., Ltd. Systems and methods for machine learned image conversion
US11379951B2 (en) 2020-03-25 2022-07-05 Nintendo Co., Ltd. Systems and methods for machine learned image conversion
CN113344199B (zh) * 2021-06-17 2024-05-03 阿波罗智联(北京)科技有限公司 用于训练可分离卷积网络的方法、路侧设备及云控平台
WO2023281371A1 (en) * 2021-07-04 2023-01-12 Numenta, Inc. Hardware architecture for processing tensors with complementary sparsity
US20250138820A1 (en) * 2023-10-26 2025-05-01 Etched.ai, Inc. Model-specific asic compilation using fused kernel replacement

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US20170103309A1 (en) * 2015-10-08 2017-04-13 International Business Machines Corporation Acceleration of convolutional neural network training using stochastic perforation
WO2017132830A1 (en) * 2016-02-02 2017-08-10 Xiaogang Wang Methods and systems for cnn network adaption and object online tracking
CN107330463A (zh) * 2017-06-29 2017-11-07 南京信息工程大学 基于cnn多特征联合和多核稀疏表示的车型识别方法
US20170337471A1 (en) * 2016-05-18 2017-11-23 Nec Laboratories America, Inc. Passive pruning of filters in a convolutional neural network

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US9424513B2 (en) * 2011-11-09 2016-08-23 Qualcomm Incorporated Methods and apparatus for neural component memory transfer of a referenced pattern by including neurons to output a pattern substantially the same as the referenced pattern
CN104077612B (zh) * 2014-07-15 2017-09-22 中国科学院合肥物质科学研究院 一种基于多特征稀疏表示技术的害虫图像识别方法
US9652817B2 (en) 2015-03-12 2017-05-16 Samsung Electronics Co., Ltd. Automated compute kernel fusion, resizing, and interleave
CN105046193B (zh) * 2015-06-05 2018-07-10 上海大学 一种基于融合稀疏表示矩阵的人体动作识别方法
US9741107B2 (en) * 2015-06-05 2017-08-22 Sony Corporation Full reference image quality assessment based on convolutional neural network
US9972063B2 (en) 2015-07-30 2018-05-15 International Business Machines Corporation Pipelined approach to fused kernels for optimization of machine learning workloads on graphical processing units
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US20170103309A1 (en) * 2015-10-08 2017-04-13 International Business Machines Corporation Acceleration of convolutional neural network training using stochastic perforation
WO2017132830A1 (en) * 2016-02-02 2017-08-10 Xiaogang Wang Methods and systems for cnn network adaption and object online tracking
US20170337471A1 (en) * 2016-05-18 2017-11-23 Nec Laboratories America, Inc. Passive pruning of filters in a convolutional neural network
CN107330463A (zh) * 2017-06-29 2017-11-07 南京信息工程大学 基于cnn多特征联合和多核稀疏表示的车型识别方法

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Publication number Publication date
JP7179850B2 (ja) 2022-11-29
CN111344720A (zh) 2020-06-26
JP2021507345A (ja) 2021-02-22
GB202010475D0 (en) 2020-08-19
US20190188526A1 (en) 2019-06-20
DE112018006377T5 (de) 2020-08-20
WO2019116291A1 (en) 2019-06-20
US10740659B2 (en) 2020-08-11

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