Hardware Neural Network for a Visual Inspection System

Seungwoo CHUN; Yoshihiro HAYAKAWA; Koji NAKAJIMA

doi:10.1093/ietfec/e91-a.4.935

Hardware Neural Network for a Visual Inspection System

Seungwoo CHUN, Yoshihiro HAYAKAWA, Koji NAKAJIMA

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The visual inspection of defects in products is heavily dependent on human experience and instinct. In this situation, it is difficult to reduce the production costs and to shorten the inspection time and hence the total process time. Consequently people involved in this area desire an automatic inspection system. In this paper, we propose a hardware neural network, which is expected to provide high-speed operation for automatic inspection of products. Since neural networks can learn, this is a suitable method for self-adjustment of criteria for classification. To achieve high-speed operation, we use parallel and pipelining techniques. Furthermore, we use a piecewise linear function instead of a conventional activation function in order to save hardware resources. Consequently, our proposed hardware neural network achieved 6GCPS and 2GCUPS, which in our test sample proved to be sufficiently fast.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.4 pp.935-942

Publication Date: 2008/04/01

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Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e91-a.4.935

Type of Manuscript: Special Section PAPER (Special Section on Selected Papers from the 20th Workshop on Circuits and Systems in Karuizawa)

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Seungwoo CHUN, Yoshihiro HAYAKAWA, Koji NAKAJIMA, "Hardware Neural Network for a Visual Inspection System" in IEICE TRANSACTIONS on Fundamentals, vol. E91-A, no. 4, pp. 935-942, April 2008, doi: 10.1093/ietfec/e91-a.4.935.
Abstract: The visual inspection of defects in products is heavily dependent on human experience and instinct. In this situation, it is difficult to reduce the production costs and to shorten the inspection time and hence the total process time. Consequently people involved in this area desire an automatic inspection system. In this paper, we propose a hardware neural network, which is expected to provide high-speed operation for automatic inspection of products. Since neural networks can learn, this is a suitable method for self-adjustment of criteria for classification. To achieve high-speed operation, we use parallel and pipelining techniques. Furthermore, we use a piecewise linear function instead of a conventional activation function in order to save hardware resources. Consequently, our proposed hardware neural network achieved 6GCPS and 2GCUPS, which in our test sample proved to be sufficiently fast.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.4.935/_p

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@ARTICLE{e91-a_4_935,
author={Seungwoo CHUN, Yoshihiro HAYAKAWA, Koji NAKAJIMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Hardware Neural Network for a Visual Inspection System},
year={2008},
volume={E91-A},
number={4},
pages={935-942},
abstract={The visual inspection of defects in products is heavily dependent on human experience and instinct. In this situation, it is difficult to reduce the production costs and to shorten the inspection time and hence the total process time. Consequently people involved in this area desire an automatic inspection system. In this paper, we propose a hardware neural network, which is expected to provide high-speed operation for automatic inspection of products. Since neural networks can learn, this is a suitable method for self-adjustment of criteria for classification. To achieve high-speed operation, we use parallel and pipelining techniques. Furthermore, we use a piecewise linear function instead of a conventional activation function in order to save hardware resources. Consequently, our proposed hardware neural network achieved 6GCPS and 2GCUPS, which in our test sample proved to be sufficiently fast.},
keywords={},
doi={10.1093/ietfec/e91-a.4.935},
ISSN={1745-1337},
month={April},}

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TY - JOUR
TI - Hardware Neural Network for a Visual Inspection System
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 935
EP - 942
AU - Seungwoo CHUN
AU - Yoshihiro HAYAKAWA
AU - Koji NAKAJIMA
PY - 2008
DO - 10.1093/ietfec/e91-a.4.935
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2008
AB - The visual inspection of defects in products is heavily dependent on human experience and instinct. In this situation, it is difficult to reduce the production costs and to shorten the inspection time and hence the total process time. Consequently people involved in this area desire an automatic inspection system. In this paper, we propose a hardware neural network, which is expected to provide high-speed operation for automatic inspection of products. Since neural networks can learn, this is a suitable method for self-adjustment of criteria for classification. To achieve high-speed operation, we use parallel and pipelining techniques. Furthermore, we use a piecewise linear function instead of a conventional activation function in order to save hardware resources. Consequently, our proposed hardware neural network achieved 6GCPS and 2GCUPS, which in our test sample proved to be sufficiently fast.
ER -