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A real-time vision system using an integrated memory array processor prototype

This paper describes a real-time vision system (RVS) architecture and performance and its use of an integrated memory array processor (IMAP) prototype. This prototype integrates eight 8-bit processors and a 144-kbit SRAM on a single chip. The RVS was developed with 64 IMAP prototypes connected in se... Full description

Journal Title: Machine vision and applications 1994-12, Vol.7 (4), p.220-228
Main Author: Fujita, Yoshihiro
Other Authors: Yamashita, Nobuyuki , Okazaki, Shin'ichiro
Format: Electronic Article Electronic Article
Language: English
ID: ISSN: 0932-8092
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recordid: cdi_crossref_primary_10_1007_BF01213412
title: A real-time vision system using an integrated memory array processor prototype
format: Article
creator:
  • Fujita, Yoshihiro
  • Yamashita, Nobuyuki
  • Okazaki, Shin'ichiro
ispartof: Machine vision and applications, 1994-12, Vol.7 (4), p.220-228
description: This paper describes a real-time vision system (RVS) architecture and performance and its use of an integrated memory array processor (IMAP) prototype. This prototype integrates eight 8-bit processors and a 144-kbit SRAM on a single chip. The RVS was developed with 64 IMAP prototypes connected in series in a 512 processor-system configuration. A host workstation can access the memory on the IMAP prototypes directly through a random access port. Images are inputted and outputted at high speed through serial access ports. The RVS performance is shown in real-time road-image processing and in a neural network simulation, as well as in low-level image processing algorithms, such as filtering, histograms, discrete cosine transform (DCT), and rotation. The RVS image processing is shown to be much faster than the video rate.
language: eng
source:
identifier: ISSN: 0932-8092
fulltext: no_fulltext
issn:
  • 0932-8092
  • 1432-1769
url: Link


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descriptionThis paper describes a real-time vision system (RVS) architecture and performance and its use of an integrated memory array processor (IMAP) prototype. This prototype integrates eight 8-bit processors and a 144-kbit SRAM on a single chip. The RVS was developed with 64 IMAP prototypes connected in series in a 512 processor-system configuration. A host workstation can access the memory on the IMAP prototypes directly through a random access port. Images are inputted and outputted at high speed through serial access ports. The RVS performance is shown in real-time road-image processing and in a neural network simulation, as well as in low-level image processing algorithms, such as filtering, histograms, discrete cosine transform (DCT), and rotation. The RVS image processing is shown to be much faster than the video rate.
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descriptionThis paper describes a real-time vision system (RVS) architecture and performance and its use of an integrated memory array processor (IMAP) prototype. This prototype integrates eight 8-bit processors and a 144-kbit SRAM on a single chip. The RVS was developed with 64 IMAP prototypes connected in series in a 512 processor-system configuration. A host workstation can access the memory on the IMAP prototypes directly through a random access port. Images are inputted and outputted at high speed through serial access ports. The RVS performance is shown in real-time road-image processing and in a neural network simulation, as well as in low-level image processing algorithms, such as filtering, histograms, discrete cosine transform (DCT), and rotation. The RVS image processing is shown to be much faster than the video rate.
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abstractThis paper describes a real-time vision system (RVS) architecture and performance and its use of an integrated memory array processor (IMAP) prototype. This prototype integrates eight 8-bit processors and a 144-kbit SRAM on a single chip. The RVS was developed with 64 IMAP prototypes connected in series in a 512 processor-system configuration. A host workstation can access the memory on the IMAP prototypes directly through a random access port. Images are inputted and outputted at high speed through serial access ports. The RVS performance is shown in real-time road-image processing and in a neural network simulation, as well as in low-level image processing algorithms, such as filtering, histograms, discrete cosine transform (DCT), and rotation. The RVS image processing is shown to be much faster than the video rate.
doi10.1007/BF01213412