Precision requirements for single-layer feed-forward neural networks

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

doi:10.1109/ICMNN.1994.593243

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 proceeding › Conference contribution › Academic › peer-review

22 Citations (Scopus)

215 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 language	English
Title of host publication	Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	-
ISBN (Print)	9780818667107
DOIs	https://doi.org/10.1109/ICMNN.1994.593243
Publication status	Published - 1994
Event	4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems, ICMNN 1994 - Turin, Italy Duration: 26 Sept 1994 → 28 Sept 1994 Conference number: 4

Publication series

Name
Publisher	IEEE
Volume	145

Conference

Conference	4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems, ICMNN 1994
Abbreviated title	ICMNN
Country/Territory	Italy
City	Turin
Period	26/09/94 → 28/09/94

Keywords

IR-56029
METIS-310955

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10.1109/ICMNN.1994.593243

00593243

Cite this

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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",

year = "1994",

doi = "10.1109/ICMNN.1994.593243",

language = "English",

isbn = "9780818667107",

publisher = "IEEE",

pages = "--",

booktitle = "Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994",

address = "United States",

note = "4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems, ICMNN 1994, ICMNN ; Conference date: 26-09-1994 Through 28-09-1994",

}

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 et al.
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 proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Precision requirements for single-layer feed-forward neural networks

AU - Annema, Anne J.

AU - Hoen, K.

AU - Hoen, Klaas

AU - Wallinga, Hans

N1 - Conference code: 4

PY - 1994

Y1 - 1994

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

U2 - 10.1109/ICMNN.1994.593243

DO - 10.1109/ICMNN.1994.593243

M3 - Conference contribution

SN - 9780818667107

SP - -

BT - Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 1994

PB - IEEE

CY - Piscataway, NJ, USA

T2 - 4th International Conference on Microelectronics for Neural Networks and Fuzzy Systems, ICMNN 1994

Y2 - 26 September 1994 through 28 September 1994

ER -

Precision requirements for single-layer feed-forward neural networks

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