Precision requirements for single-layer feed-forward neural networks

Anne J. Annema, K. Hoen, Klaas Hoen, Hans Wallinga

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    20 Citations (Scopus)
    64 Downloads (Pure)

    Abstract

    This paper presents a mathematical analysis of the effect of limited precision analog hardware for weight adaptation to be used in on-chip learning feedforward neural networks. Easy-to-read equations and simple worst-case estimations for the maximum tolerable imprecision are presented. As an application of the analysis, a worst-case estimation on the minimum size of the weight storage capacitors is presented
    Original languageEnglish
    Title of host publicationProceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994
    Place of PublicationPiscataway, NJ, USA
    PublisherIEEE
    Pages-
    ISBN (Print)9780818667107
    DOIs
    Publication statusPublished - 1994
    Event4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems, ICMNN 1994 - Turin, Italy
    Duration: 26 Sep 199428 Sep 1994
    Conference number: 4

    Publication series

    Name
    PublisherIEEE
    Volume145

    Conference

    Conference4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems, ICMNN 1994
    Abbreviated titleICMNN
    CountryItaly
    CityTurin
    Period26/09/9428/09/94

    Fingerprint

    Feedforward neural networks
    Capacitor storage
    Hardware

    Keywords

    • IR-56029
    • METIS-310955

    Cite this

    Annema, A. J., Hoen, K., Hoen, K., & Wallinga, H. (1994). Precision requirements for single-layer feed-forward neural networks. In Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994 (pp. -). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/ICMNN.1994.593243
    Annema, Anne J. ; Hoen, K. ; Hoen, Klaas ; Wallinga, Hans. / Precision requirements for single-layer feed-forward neural networks. Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994. Piscataway, NJ, USA : IEEE, 1994. pp. -
    @inproceedings{6435014321bb474195b31e6465e25660,
    title = "Precision requirements for single-layer feed-forward neural networks",
    abstract = "This paper presents a mathematical analysis of the effect of limited precision analog hardware for weight adaptation to be used in on-chip learning feedforward neural networks. Easy-to-read equations and simple worst-case estimations for the maximum tolerable imprecision are presented. As an application of the analysis, a worst-case estimation on the minimum size of the weight storage capacitors is presented",
    keywords = "IR-56029, METIS-310955",
    author = "Annema, {Anne J.} and K. Hoen and Klaas Hoen and Hans Wallinga",
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    publisher = "IEEE",
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    Annema, AJ, Hoen, K, Hoen, K & Wallinga, H 1994, Precision requirements for single-layer feed-forward neural networks. in Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994. IEEE, Piscataway, NJ, USA, pp. -, 4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems, ICMNN 1994, Turin, Italy, 26/09/94. https://doi.org/10.1109/ICMNN.1994.593243

    Precision requirements for single-layer feed-forward neural networks. / Annema, Anne J.; Hoen, K.; Hoen, Klaas; Wallinga, Hans.

    Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994. Piscataway, NJ, USA : IEEE, 1994. p. -.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    TY - GEN

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    AU - Hoen, Klaas

    AU - Wallinga, Hans

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    N2 - This paper presents a mathematical analysis of the effect of limited precision analog hardware for weight adaptation to be used in on-chip learning feedforward neural networks. Easy-to-read equations and simple worst-case estimations for the maximum tolerable imprecision are presented. As an application of the analysis, a worst-case estimation on the minimum size of the weight storage capacitors is presented

    AB - This paper presents a mathematical analysis of the effect of limited precision analog hardware for weight adaptation to be used in on-chip learning feedforward neural networks. Easy-to-read equations and simple worst-case estimations for the maximum tolerable imprecision are presented. As an application of the analysis, a worst-case estimation on the minimum size of the weight storage capacitors is presented

    KW - IR-56029

    KW - METIS-310955

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    Annema AJ, Hoen K, Hoen K, Wallinga H. Precision requirements for single-layer feed-forward neural networks. In Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994. Piscataway, NJ, USA: IEEE. 1994. p. - https://doi.org/10.1109/ICMNN.1994.593243