A Super Parallel Sorter Using a Binary Neural Network with AND-OR Synaptic Connections View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1992

AUTHORS

Manabu Yamada , Tohru Nakagawa , Hajime Kitagawa

ABSTRACT

This paper presents an ultra-high-speed sorter based upon a simplified parallel sorting algorithm using a binary neural network which consists both of binary neurons and of AND-OR synaptic connections to solve sorting problems at two and only two clock cycles. Our simplified algorithm is based on the super parallel sorting algorithm proposed by Takefuji and Lee. Nevertheless, our algorithm does not need any adders, while Takefuji’s algorithm needs n x (n — 1) analog adders of which each has multiple input ports. For an example of the simplified parallel sorter, a hardware design and its implementation will be introduced in this paper, which performs a sorting operation at two clock cycles. Both results of a logic circuit simulation and of an algorithm simulation show the justice of our hardware implementation even if in the practical size of the problem. More... »

PAGES

127-131

Book

TITLE

Analog VLSI Neural Networks

ISBN

978-1-4613-6592-1
978-1-4615-3582-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4615-3582-9_11

DOI

http://dx.doi.org/10.1007/978-1-4615-3582-9_11

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024627656


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Toyota Technological Institute", 
          "id": "https://www.grid.ac/institutes/grid.265129.b", 
          "name": [
            "Toyota Technological Institute, Nagoya 468, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yamada", 
        "givenName": "Manabu", 
        "id": "sg:person.07410611446.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07410611446.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Toyota Technological Institute", 
          "id": "https://www.grid.ac/institutes/grid.265129.b", 
          "name": [
            "Toyota Technological Institute, Nagoya 468, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nakagawa", 
        "givenName": "Tohru", 
        "id": "sg:person.07713425233.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07713425233.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Toyota Technological Institute", 
          "id": "https://www.grid.ac/institutes/grid.265129.b", 
          "name": [
            "Toyota Technological Institute, Nagoya 468, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kitagawa", 
        "givenName": "Hajime", 
        "id": "sg:person.010511005633.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010511005633.94"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1073/pnas.81.10.3088", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049596495"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/31.62417", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061153280"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1992", 
    "datePublishedReg": "1992-01-01", 
    "description": "This paper presents an ultra-high-speed sorter based upon a simplified parallel sorting algorithm using a binary neural network which consists both of binary neurons and of AND-OR synaptic connections to solve sorting problems at two and only two clock cycles. Our simplified algorithm is based on the super parallel sorting algorithm proposed by Takefuji and Lee. Nevertheless, our algorithm does not need any adders, while Takefuji\u2019s algorithm needs n x (n \u2014 1) analog adders of which each has multiple input ports. For an example of the simplified parallel sorter, a hardware design and its implementation will be introduced in this paper, which performs a sorting operation at two clock cycles. Both results of a logic circuit simulation and of an algorithm simulation show the justice of our hardware implementation even if in the practical size of the problem.", 
    "editor": [
      {
        "familyName": "Takefuji", 
        "givenName": "Yoshiyasu", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-4615-3582-9_11", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-1-4613-6592-1", 
        "978-1-4615-3582-9"
      ], 
      "name": "Analog VLSI Neural Networks", 
      "type": "Book"
    }, 
    "name": "A Super Parallel Sorter Using a Binary Neural Network with AND-OR Synaptic Connections", 
    "pagination": "127-131", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-4615-3582-9_11"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "7553c5db73799f566e4ad88668030a4c8aeff08f75e75485fa36fdeebbcdf4f2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1024627656"
        ]
      }
    ], 
    "publisher": {
      "location": "Boston, MA", 
      "name": "Springer US", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-4615-3582-9_11", 
      "https://app.dimensions.ai/details/publication/pub.1024627656"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T13:28", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8664_00000258.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-1-4615-3582-9_11"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-1-4615-3582-9_11'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-4615-3582-9_11'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-4615-3582-9_11'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-4615-3582-9_11'


 

This table displays all metadata directly associated to this object as RDF triples.

85 TRIPLES      23 PREDICATES      29 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-4615-3582-9_11 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N270a61da46c949cfb276a4029c419487
4 schema:citation https://doi.org/10.1073/pnas.81.10.3088
5 https://doi.org/10.1109/31.62417
6 schema:datePublished 1992
7 schema:datePublishedReg 1992-01-01
8 schema:description This paper presents an ultra-high-speed sorter based upon a simplified parallel sorting algorithm using a binary neural network which consists both of binary neurons and of AND-OR synaptic connections to solve sorting problems at two and only two clock cycles. Our simplified algorithm is based on the super parallel sorting algorithm proposed by Takefuji and Lee. Nevertheless, our algorithm does not need any adders, while Takefuji’s algorithm needs n x (n — 1) analog adders of which each has multiple input ports. For an example of the simplified parallel sorter, a hardware design and its implementation will be introduced in this paper, which performs a sorting operation at two clock cycles. Both results of a logic circuit simulation and of an algorithm simulation show the justice of our hardware implementation even if in the practical size of the problem.
9 schema:editor N26819275612e4c65be961730f1c776bc
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf N1caa08afdd3b4544b5e2707906b38576
14 schema:name A Super Parallel Sorter Using a Binary Neural Network with AND-OR Synaptic Connections
15 schema:pagination 127-131
16 schema:productId N251d0b025482405497176cd6554f8492
17 N51de806c039b40a49d847e5a021e295b
18 Nd979560c8a694a3bb7c773bc62934fe2
19 schema:publisher N9bda4ea431b945748c6e5c95c368255d
20 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024627656
21 https://doi.org/10.1007/978-1-4615-3582-9_11
22 schema:sdDatePublished 2019-04-15T13:28
23 schema:sdLicense https://scigraph.springernature.com/explorer/license/
24 schema:sdPublisher N623e8bce139c4c24b1974ce4f55999d3
25 schema:url http://link.springer.com/10.1007/978-1-4615-3582-9_11
26 sgo:license sg:explorer/license/
27 sgo:sdDataset chapters
28 rdf:type schema:Chapter
29 N1caa08afdd3b4544b5e2707906b38576 schema:isbn 978-1-4613-6592-1
30 978-1-4615-3582-9
31 schema:name Analog VLSI Neural Networks
32 rdf:type schema:Book
33 N251d0b025482405497176cd6554f8492 schema:name dimensions_id
34 schema:value pub.1024627656
35 rdf:type schema:PropertyValue
36 N26819275612e4c65be961730f1c776bc rdf:first Nb13b10a0b9244803b3992a56811a9a14
37 rdf:rest rdf:nil
38 N270a61da46c949cfb276a4029c419487 rdf:first sg:person.07410611446.10
39 rdf:rest Ne93f05b6b1bb478b9ebc69d7b48080dc
40 N2b420c36a77a47999f2baf085b24feee rdf:first sg:person.010511005633.94
41 rdf:rest rdf:nil
42 N51de806c039b40a49d847e5a021e295b schema:name readcube_id
43 schema:value 7553c5db73799f566e4ad88668030a4c8aeff08f75e75485fa36fdeebbcdf4f2
44 rdf:type schema:PropertyValue
45 N623e8bce139c4c24b1974ce4f55999d3 schema:name Springer Nature - SN SciGraph project
46 rdf:type schema:Organization
47 N9bda4ea431b945748c6e5c95c368255d schema:location Boston, MA
48 schema:name Springer US
49 rdf:type schema:Organisation
50 Nb13b10a0b9244803b3992a56811a9a14 schema:familyName Takefuji
51 schema:givenName Yoshiyasu
52 rdf:type schema:Person
53 Nd979560c8a694a3bb7c773bc62934fe2 schema:name doi
54 schema:value 10.1007/978-1-4615-3582-9_11
55 rdf:type schema:PropertyValue
56 Ne93f05b6b1bb478b9ebc69d7b48080dc rdf:first sg:person.07713425233.07
57 rdf:rest N2b420c36a77a47999f2baf085b24feee
58 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
59 schema:name Information and Computing Sciences
60 rdf:type schema:DefinedTerm
61 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
62 schema:name Artificial Intelligence and Image Processing
63 rdf:type schema:DefinedTerm
64 sg:person.010511005633.94 schema:affiliation https://www.grid.ac/institutes/grid.265129.b
65 schema:familyName Kitagawa
66 schema:givenName Hajime
67 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010511005633.94
68 rdf:type schema:Person
69 sg:person.07410611446.10 schema:affiliation https://www.grid.ac/institutes/grid.265129.b
70 schema:familyName Yamada
71 schema:givenName Manabu
72 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07410611446.10
73 rdf:type schema:Person
74 sg:person.07713425233.07 schema:affiliation https://www.grid.ac/institutes/grid.265129.b
75 schema:familyName Nakagawa
76 schema:givenName Tohru
77 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07713425233.07
78 rdf:type schema:Person
79 https://doi.org/10.1073/pnas.81.10.3088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049596495
80 rdf:type schema:CreativeWork
81 https://doi.org/10.1109/31.62417 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061153280
82 rdf:type schema:CreativeWork
83 https://www.grid.ac/institutes/grid.265129.b schema:alternateName Toyota Technological Institute
84 schema:name Toyota Technological Institute, Nagoya 468, Japan
85 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...