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Cited by 142Advanced Memory and Neural ComputingAnalog and Mixed-Signal Circuit DesignNeural Networks and Applications

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

An analog neural network processor with programmable topology

Boser, B.E., Sackinger, E., Bromley, J., Le Cun, Y., Jackel, L.D.·1991·IEEE Journal of Solid-State Circuits
DOI 10.1109/4.104196
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Abstract

The architecture, implementation, and applications of a special-purpose neural network processor are described. The chip performs over 2000 multiplications and additions simultaneously. Its data path is particularly suitable for the convolutional topologies that are typical in classification networks, but can also be configured for fully connected or feedback topologies. Resources can be multiplexed to permit implementation of networks with several hundreds of thousands of connections on a single chip. Computations are performed with 6 b accuracy for the weights and 3 b for the neuron states. Analog processing is used internally for reduced power dissipation and higher density, but all input/output is digital to simplify system integration. The practicality of the chip is demonstrated with an implementation of a neural network for optical character recognition. This network contains over 130000 connections and was evaluated in 1 ms.>

Overview

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Registry

Crossref · 10.1109/4.104196

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Globally trained, variable-size TDNN/HMM hybrids have been used for speech recognition and online handwriting recognition.

    “Globally trained, variablesize TDNN/HMM hybrids have been used for speech recognition and online handwriting recognition [67], [77], [89], [90].”

    From Gradient-based learning applied to document recognition (1998) · cites this paper as [89]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 18

  1. 10.1109/ISSCC.1991.689118 · DOI
  2. alspector, 1991, Neural Information Processing Systems, 3, 1015
  3. 10.1109/ICSMC.1990.142119 · DOI
  4. 10.1109/72.80206 · DOI
  5. 10.1109/ISSCC.1990.110168 · DOI
  6. 10.1109/ISSCC.1989.48217 · DOI
  7. 10.1109/ISSCC.1990.110167 · DOI
  8. borgstrom, 1990, programmable current-mode neural network for implementation in analogue mos vlsi, Circuits Devices and Systems IEE Proceedings G, 137, 175, 10.1049/ip-g-2.1990.0027 · DOI
  9. 10.1109/IJCNN.1991.155214 · DOI
  10. 10.1109/40.16779 · DOI
  11. 10.1016/0031-3203(82)90024-3 · DOI
  12. le cun, 1990, Neural Information Processing Systems, 2, 396
  13. 10.1162/neco.1989.1.1.1 · DOI
  14. milito, 1991, Advances in Neural Information Processing Systems 3, 3
  15. 10.1016/0031-3203(91)90081-F · DOI
  16. 10.1109/MASSP.1987.1165576 · DOI
  17. 10.1088/0305-4470/22/12/019 · DOI
  18. rumelhart, 1986, Parallel Distributed Processing Explorations in the Microstructures of Cognition, 1, 10.7551/mitpress/5236.001.0001 · DOI · Paper page

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Metadata

Authors
Boser, B. E., Sackinger, E., Bromley, J., Le Cun, Y., & Jackel, L. D.
Affiliations
AT&T (United States) (US)
ORCID
Venue
IEEE Journal of Solid-State Circuits
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1991
Volume
26
Issue
12
Pages
2017-2025
ISSN
0018-9200 (ISSN-L)
DOI
10.1109/4.104196
OpenAlex
W1999497791
Cited by
142
References
1
Citations by year
2026: 1 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 2 · 2021: 2 · 2020: 21 · 2019: 7 · 2018: 8 · 2017: 3 · 2016: 3 · 2015: 5 · 2014: 6 · 2013: 3 · 2012: 3
FWCI
4.48
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Journal of Solid-State Circuits (publishedVersion)
  • Open Research Online (The Open University) (acceptedVersion)
  • HAL (submittedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
convolutional neural networks, classification networks, fully connected networks, feedback network, digital-to-analog conversion, low power consumption, optical character recognition, VLSI implementation

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