A Memory-Based Parallel Processor for Vector Quantization: FMPP-VQ

Kazutoshi KOBAYASHI; Masayoshi KINOSHITA; Hidetoshi ONODERA; Keikichi TAMARU

A Memory-Based Parallel Processor for Vector Quantization: FMPP-VQ

Kazutoshi KOBAYASHI, Masayoshi KINOSHITA, Hidetoshi ONODERA, Keikichi TAMARU

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We propose a memory-based processor called a Functional Memory Type Parallel Processor for vector quantization (FMPP-VQ). The FMPP-VQ is intended for low bit-rate image compression using vector quantization. It accelerates the nearest neighbor search on vector quantization. In the nearest neighbor search, we look for a vector nearest to an input one among a large number of code vectors. The FMPP-VQ has as many PEs (processing elements, also called "blocks") as code vectors. Thus distances between an input vector and code vectors are computed simultaneously in every PE. The minimum value of all the distances is searched in parallel, as in conventional CAMs. The computation time does not depend on the number of code vectors. In this paper, we explain the detail of the architecture of the FMPP-VQ, its performance and its layout density. We designed and fabricated an LSI including four PEs. The test results and performance estimation of the LSI are also reported.

Publication: IEICE TRANSACTIONS on Electronics Vol.E80-C No.7 pp.970-975

Publication Date: 1997/07/25

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Type of Manuscript: Special Section PAPER (Special Issue on New Concept Device and Novel Architecture LSIs)

Category: Multi Processors

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Kazutoshi KOBAYASHI, Masayoshi KINOSHITA, Hidetoshi ONODERA, Keikichi TAMARU, "A Memory-Based Parallel Processor for Vector Quantization: FMPP-VQ" in IEICE TRANSACTIONS on Electronics, vol. E80-C, no. 7, pp. 970-975, July 1997, doi: .
Abstract: We propose a memory-based processor called a Functional Memory Type Parallel Processor for vector quantization (FMPP-VQ). The FMPP-VQ is intended for low bit-rate image compression using vector quantization. It accelerates the nearest neighbor search on vector quantization. In the nearest neighbor search, we look for a vector nearest to an input one among a large number of code vectors. The FMPP-VQ has as many PEs (processing elements, also called "blocks") as code vectors. Thus distances between an input vector and code vectors are computed simultaneously in every PE. The minimum value of all the distances is searched in parallel, as in conventional CAMs. The computation time does not depend on the number of code vectors. In this paper, we explain the detail of the architecture of the FMPP-VQ, its performance and its layout density. We designed and fabricated an LSI including four PEs. The test results and performance estimation of the LSI are also reported.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/e80-c_7_970/_p

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@ARTICLE{e80-c_7_970,
author={Kazutoshi KOBAYASHI, Masayoshi KINOSHITA, Hidetoshi ONODERA, Keikichi TAMARU, },
journal={IEICE TRANSACTIONS on Electronics},
title={A Memory-Based Parallel Processor for Vector Quantization: FMPP-VQ},
year={1997},
volume={E80-C},
number={7},
pages={970-975},
abstract={We propose a memory-based processor called a Functional Memory Type Parallel Processor for vector quantization (FMPP-VQ). The FMPP-VQ is intended for low bit-rate image compression using vector quantization. It accelerates the nearest neighbor search on vector quantization. In the nearest neighbor search, we look for a vector nearest to an input one among a large number of code vectors. The FMPP-VQ has as many PEs (processing elements, also called "blocks") as code vectors. Thus distances between an input vector and code vectors are computed simultaneously in every PE. The minimum value of all the distances is searched in parallel, as in conventional CAMs. The computation time does not depend on the number of code vectors. In this paper, we explain the detail of the architecture of the FMPP-VQ, its performance and its layout density. We designed and fabricated an LSI including four PEs. The test results and performance estimation of the LSI are also reported.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - A Memory-Based Parallel Processor for Vector Quantization: FMPP-VQ
T2 - IEICE TRANSACTIONS on Electronics
SP - 970
EP - 975
AU - Kazutoshi KOBAYASHI
AU - Masayoshi KINOSHITA
AU - Hidetoshi ONODERA
AU - Keikichi TAMARU
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E80-C
IS - 7
JA - IEICE TRANSACTIONS on Electronics
Y1 - July 1997
AB - We propose a memory-based processor called a Functional Memory Type Parallel Processor for vector quantization (FMPP-VQ). The FMPP-VQ is intended for low bit-rate image compression using vector quantization. It accelerates the nearest neighbor search on vector quantization. In the nearest neighbor search, we look for a vector nearest to an input one among a large number of code vectors. The FMPP-VQ has as many PEs (processing elements, also called "blocks") as code vectors. Thus distances between an input vector and code vectors are computed simultaneously in every PE. The minimum value of all the distances is searched in parallel, as in conventional CAMs. The computation time does not depend on the number of code vectors. In this paper, we explain the detail of the architecture of the FMPP-VQ, its performance and its layout density. We designed and fabricated an LSI including four PEs. The test results and performance estimation of the LSI are also reported.
ER -