FR3117645B1 - Mise à profit de la faible densité de données ou de poids non-nuls dans un calculateur de somme pondérée - Google Patents

Mise à profit de la faible densité de données ou de poids non-nuls dans un calculateur de somme pondérée Download PDF

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Publication number
FR3117645B1
FR3117645B1 FR2013363A FR2013363A FR3117645B1 FR 3117645 B1 FR3117645 B1 FR 3117645B1 FR 2013363 A FR2013363 A FR 2013363A FR 2013363 A FR2013363 A FR 2013363A FR 3117645 B1 FR3117645 B1 FR 3117645B1
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Prior art keywords
data
weighted sum
buffer memory
taking advantage
low data
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FR2013363A
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FR3117645A1 (fr
Inventor
Michel Harrand
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Commissariat a lEnergie Atomique et aux Energies Alternatives CEA
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Commissariat a lEnergie Atomique CEA
Commissariat a lEnergie Atomique et aux Energies Alternatives CEA
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Priority to FR2013363A priority Critical patent/FR3117645B1/fr
Priority to EP21839468.2A priority patent/EP4264497A1/fr
Priority to US18/267,070 priority patent/US20240054330A1/en
Priority to PCT/EP2021/085864 priority patent/WO2022129156A1/fr
Publication of FR3117645A1 publication Critical patent/FR3117645A1/fr
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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
    • G06N3/06—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; 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
    • 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
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections

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  • 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)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Neurology (AREA)
  • Complex Calculations (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

Circuit de calcul pour calculer une somme pondérée d’un ensemble de premières données par au moins un circuit de gestion de parcimonie comprenant  une première mémoire tampon pour stocker tout ou une partie des premières données délivrées séquentiellement et une seconde mémoire tampon pour stocker tout ou partie des secondes données délivrées séquentiellement. Le circuit de gestion de parcimonie comprenant en outre un premier circuit de traitement apte : à analyser les premières données pour rechercher les premières données non-nulles et définir un premier indicateur de saut (is1) entre deux données non nulles successives, et à commander le transfert vers le circuit de distribution d’une première donnée lue dans la première mémoire tampon de données en fonction dudit premier indicateur de saut. Le circuit de gestion de parcimonie comprenant en outre un second circuit de traitement apte à commander le transfert vers le circuit de distribution d’une seconde donnée lue dans la deuxième mémoire tampon de données en fonction dudit premier indicateur de saut.
FR2013363A 2020-12-16 2020-12-16 Mise à profit de la faible densité de données ou de poids non-nuls dans un calculateur de somme pondérée Active FR3117645B1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
FR2013363A FR3117645B1 (fr) 2020-12-16 2020-12-16 Mise à profit de la faible densité de données ou de poids non-nuls dans un calculateur de somme pondérée
EP21839468.2A EP4264497A1 (fr) 2020-12-16 2021-12-15 Mise a profit de la faible densite de donnees ou de poids non-nuls dans un calculateur de somme ponderee
US18/267,070 US20240054330A1 (en) 2020-12-16 2021-12-15 Exploitation of low data density or nonzero weights in a weighted sum computer
PCT/EP2021/085864 WO2022129156A1 (fr) 2020-12-16 2021-12-15 Mise a profit de la faible densite de donnees ou de poids non-nuls dans un calculateur de somme ponderee

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR2013363A FR3117645B1 (fr) 2020-12-16 2020-12-16 Mise à profit de la faible densité de données ou de poids non-nuls dans un calculateur de somme pondérée
FR2013363 2020-12-16

Publications (2)

Publication Number Publication Date
FR3117645A1 FR3117645A1 (fr) 2022-06-17
FR3117645B1 true FR3117645B1 (fr) 2023-08-25

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FR2013363A Active FR3117645B1 (fr) 2020-12-16 2020-12-16 Mise à profit de la faible densité de données ou de poids non-nuls dans un calculateur de somme pondérée

Country Status (4)

Country Link
US (1) US20240054330A1 (fr)
EP (1) EP4264497A1 (fr)
FR (1) FR3117645B1 (fr)
WO (1) WO2022129156A1 (fr)

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10725740B2 (en) * 2017-08-31 2020-07-28 Qualcomm Incorporated Providing efficient multiplication of sparse matrices in matrix-processor-based devices
GB2568102B (en) * 2017-11-06 2021-04-14 Imagination Tech Ltd Exploiting sparsity in a neural network

Also Published As

Publication number Publication date
WO2022129156A1 (fr) 2022-06-23
FR3117645A1 (fr) 2022-06-17
US20240054330A1 (en) 2024-02-15
EP4264497A1 (fr) 2023-10-25

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