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A 218 GOPS neural network accelerator based on a novel cost-efficient surrogate gradient scheme for pattern classification.

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Title: A 218 GOPS neural network accelerator based on a novel cost-efficient surrogate gradient scheme for pattern classification.
Authors: Siddique, Ali1,2, Iqbal, Muhammad Azhar1,3, azhar.iqbal@lancaster.ac.uk, Aleem, Muhammad4, Islam, Muhammad Arshad4
Source: Microprocessors & Microsystems; Jun2023, Vol. 99, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 163795075
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=163795075
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.micpro.2023.104831
    Languages:
      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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      – TitleFull: A 218 GOPS neural network accelerator based on a novel cost-efficient surrogate gradient scheme for pattern classification.
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            NameFull: Siddique, Ali
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            NameFull: Iqbal, Muhammad Azhar
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            NameFull: Aleem, Muhammad
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            NameFull: Islam, Muhammad Arshad
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            – D: 01
              M: 06
              Text: Jun2023
              Type: published
              Y: 2023
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              Value: 01419331
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              Value: 99
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            – TitleFull: Microprocessors & Microsystems
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