EP3158509A4 - Memristive neuronale nanofasernetzwerke - Google Patents

Memristive neuronale nanofasernetzwerke Download PDF

Info

Publication number
EP3158509A4
EP3158509A4 EP15810294.7A EP15810294A EP3158509A4 EP 3158509 A4 EP3158509 A4 EP 3158509A4 EP 15810294 A EP15810294 A EP 15810294A EP 3158509 A4 EP3158509 A4 EP 3158509A4
Authority
EP
European Patent Office
Prior art keywords
neural networks
memristive nanofiber
memristive
nanofiber neural
networks
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.)
Ceased
Application number
EP15810294.7A
Other languages
English (en)
French (fr)
Other versions
EP3158509A1 (de
Inventor
Juan Claudio Nino
Jack D. KENDALL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Florida
University of Florida Research Foundation Inc
Original Assignee
University of Florida
University of Florida Research Foundation Inc
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 University of Florida, University of Florida Research Foundation Inc filed Critical University of Florida
Publication of EP3158509A1 publication Critical patent/EP3158509A1/de
Publication of EP3158509A4 publication Critical patent/EP3158509A4/de
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C13/00—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00
    • G11C13/0002—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00 using resistive RAM [RRAM] elements
    • G11C13/0007—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00 using resistive RAM [RRAM] elements comprising metal oxide memory material, e.g. perovskites
    • 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
    • G06N3/065—Analogue 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield 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
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • 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
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Software Systems (AREA)
  • Neurology (AREA)
  • Chemical & Material Sciences (AREA)
  • Materials Engineering (AREA)
  • Metal-Oxide And Bipolar Metal-Oxide Semiconductor Integrated Circuits (AREA)
  • Semiconductor Memories (AREA)
  • Micromachines (AREA)
EP15810294.7A 2014-06-19 2015-06-05 Memristive neuronale nanofasernetzwerke Ceased EP3158509A4 (de)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201462014201P 2014-06-19 2014-06-19
PCT/US2015/034414 WO2015195365A1 (en) 2014-06-19 2015-06-05 Memristive nanofiber neural netwoks

Publications (2)

Publication Number Publication Date
EP3158509A1 EP3158509A1 (de) 2017-04-26
EP3158509A4 true EP3158509A4 (de) 2018-02-28

Family

ID=54935975

Family Applications (1)

Application Number Title Priority Date Filing Date
EP15810294.7A Ceased EP3158509A4 (de) 2014-06-19 2015-06-05 Memristive neuronale nanofasernetzwerke

Country Status (6)

Country Link
EP (1) EP3158509A4 (de)
JP (1) JP6571692B2 (de)
KR (1) KR20170019414A (de)
AU (1) AU2015277645B2 (de)
BR (1) BR112016029682A2 (de)
WO (1) WO2015195365A1 (de)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10198691B2 (en) 2014-06-19 2019-02-05 University Of Florida Research Foundation, Inc. Memristive nanofiber neural networks
CN107533668B (zh) 2016-03-11 2021-01-26 慧与发展有限责任合伙企业 用于计算神经网络的节点值的硬件加速器和方法
EP3631800A4 (de) * 2017-05-22 2021-04-07 University of Florida Research Foundation Tiefenlernen in zweiteiligen memristiven netzwerken
WO2019195660A1 (en) 2018-04-05 2019-10-10 Rain Neuromorphics Inc. Systems and methods for efficient matrix multiplication
US11450712B2 (en) 2020-02-18 2022-09-20 Rain Neuromorphics Inc. Memristive device
CN120046673B (zh) * 2025-04-23 2025-09-12 武汉工程大学 一种具有部分强化的操作性条件反射的忆阻神经网络电路

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140151623A1 (en) * 2012-12-04 2014-06-05 Samsung Electronics Co., Ltd. Resistive random access memory devices formed on fiber and methods of manufacturing the same

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR0185757B1 (ko) * 1994-02-14 1999-05-15 정호선 혼돈 순환 신경회로망의 학습방법
JPH09185596A (ja) * 1996-01-08 1997-07-15 Ricoh Co Ltd パルス密度型信号処理回路網における結合係数更新方法
US7392230B2 (en) * 2002-03-12 2008-06-24 Knowmtech, Llc Physical neural network liquid state machine utilizing nanotechnology
US7359888B2 (en) * 2003-01-31 2008-04-15 Hewlett-Packard Development Company, L.P. Molecular-junction-nanowire-crossbar-based neural network
WO2008042900A2 (en) * 2006-10-02 2008-04-10 University Of Florida Research Foundation, Inc. Pulse-based feature extraction for neural recordings
EP2230633A1 (de) * 2009-03-17 2010-09-22 Commissariat à l'Énergie Atomique et aux Énergies Alternatives Neuronale Netzwerkschaltung mit nanoskaligen Synapsen und CMOS-Neuronen
US8050078B2 (en) * 2009-10-27 2011-11-01 Hewlett-Packard Development Company, L.P. Nanowire-based memristor devices
US8433665B2 (en) * 2010-07-07 2013-04-30 Qualcomm Incorporated Methods and systems for three-memristor synapse with STDP and dopamine signaling
US9418331B2 (en) * 2013-10-28 2016-08-16 Qualcomm Incorporated Methods and apparatus for tagging classes using supervised learning

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140151623A1 (en) * 2012-12-04 2014-06-05 Samsung Electronics Co., Ltd. Resistive random access memory devices formed on fiber and methods of manufacturing the same

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
AMIT PRAKASH ET AL: "Resistive switching memory characteristics of Ge/GeOx nanowires and evidence of oxygen ion migration", NANOSCALE RESEARCH LETTERS, 8 May 2013 (2013-05-08), United States, pages 220 - 220, XP055744692, Retrieved from the Internet <URL:https://nanoscalereslett.springeropen.com/track/pdf/10.1186/1556-276X-8-220.pdf> [retrieved on 20201029], DOI: 10.1186/1556-276X-8-220 *
MANAN SURI ET AL: "Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses", IEEE TRANSACTIONS ON ELECTRON DEVICES, vol. 60, no. 7, 4 June 2013 (2013-06-04), US, pages 2402 - 2409, XP055440870, ISSN: 0018-9383, DOI: 10.1109/TED.2013.2263000 *
See also references of WO2015195365A1 *

Also Published As

Publication number Publication date
JP2017527000A (ja) 2017-09-14
EP3158509A1 (de) 2017-04-26
AU2015277645B2 (en) 2021-01-28
JP6571692B2 (ja) 2019-09-04
BR112016029682A2 (pt) 2018-07-10
KR20170019414A (ko) 2017-02-21
AU2015277645A1 (en) 2016-12-22
WO2015195365A1 (en) 2015-12-23

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