System And Method For Parallelizing And Accelerating Learning Machine Training And Classification Using A Massively Parallel Accelerator


Ontology type: sgo:Patent     


Patent Info

DATE

2011-02-23T00:00

AUTHORS

CADAMBI, SRIHARI

ABSTRACT

A method system for training an apparatus to recognize a pattern includes providing the apparatus with a host processor executing steps of a machine learning process; providing the apparatus with an accelerator including at least two processors; inputting training pattern data into the host processor; determining coefficient changes in the machine learning process with the host processor using the training pattern data; transferring the training data to the accelerator; determining kernel dot-products with the at least two processors of the accelerator using the training data; and transferring the dot-products back to the host processor. More... »

Related SciGraph Publications

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/2008/2746", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "name": "CADAMBI, SRIHARI", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-540-73400-0_33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004763848", 
          "https://doi.org/10.1007/978-3-540-73400-0_33"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-73400-0_33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004763848", 
          "https://doi.org/10.1007/978-3-540-73400-0_33"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/imtc.2004.1351489", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093689898"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/dicta.2005.48", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095240687"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icdar.2005.251", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095431167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wises.2006.329117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095495462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/vlsid.2007.73", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095691174"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011-02-23T00:00", 
    "description": "

A method system for training an apparatus to recognize a pattern includes providing the apparatus with a host processor executing steps of a machine learning process; providing the apparatus with an accelerator including at least two processors; inputting training pattern data into the host processor; determining coefficient changes in the machine learning process with the host processor using the training pattern data; transferring the training data to the accelerator; determining kernel dot-products with the at least two processors of the accelerator using the training data; and transferring the dot-products back to the host processor.

", "id": "sg:patent.EP-2286347-A2", "keywords": [ "classification", "method", "apparatus", "pattern", "processor", "machine", "accelerator", "coefficient", "training data", "product" ], "name": "SYSTEM AND METHOD FOR PARALLELIZING AND ACCELERATING LEARNING MACHINE TRAINING AND CLASSIFICATION USING A MASSIVELY PARALLEL ACCELERATOR", "recipient": [ { "id": "https://www.grid.ac/institutes/grid.419859.8", "type": "Organization" } ], "sameAs": [ "https://app.dimensions.ai/details/patent/EP-2286347-A2" ], "sdDataset": "patents", "sdDatePublished": "2019-04-18T10:06", "sdLicense": "https://scigraph.springernature.com/explorer/license/", "sdPublisher": { "name": "Springer Nature - SN SciGraph project", "type": "Organization" }, "sdSource": "s3://com-uberresearch-data-patents-target-20190320-rc/data/sn-export/402f166718b70575fb5d4ffe01f064d1/0000100128-0000352499/json_export_00166.jsonl", "type": "Patent" } ]
 

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/patent.EP-2286347-A2'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/patent.EP-2286347-A2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/patent.EP-2286347-A2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/patent.EP-2286347-A2'


 

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

51 TRIPLES      15 PREDICATES      30 URIs      18 LITERALS      2 BLANK NODES

Subject Predicate Object
1 sg:patent.EP-2286347-A2 schema:about anzsrc-for:2746
2 schema:author Nd8d3e8b3753b44798f8c0cc8d4f1499f
3 schema:citation sg:pub.10.1007/978-3-540-73400-0_33
4 https://doi.org/10.1109/dicta.2005.48
5 https://doi.org/10.1109/icdar.2005.251
6 https://doi.org/10.1109/imtc.2004.1351489
7 https://doi.org/10.1109/vlsid.2007.73
8 https://doi.org/10.1109/wises.2006.329117
9 schema:datePublished 2011-02-23T00:00
10 schema:description <p>A method system for training an apparatus to recognize a pattern includes providing the apparatus with a host processor executing steps of a machine learning process; providing the apparatus with an accelerator including at least two processors; inputting training pattern data into the host processor; determining coefficient changes in the machine learning process with the host processor using the training pattern data; transferring the training data to the accelerator; determining kernel dot-products with the at least two processors of the accelerator using the training data; and transferring the dot-products back to the host processor.</p>
11 schema:keywords accelerator
12 apparatus
13 classification
14 coefficient
15 machine
16 method
17 pattern
18 processor
19 product
20 training data
21 schema:name SYSTEM AND METHOD FOR PARALLELIZING AND ACCELERATING LEARNING MACHINE TRAINING AND CLASSIFICATION USING A MASSIVELY PARALLEL ACCELERATOR
22 schema:recipient https://www.grid.ac/institutes/grid.419859.8
23 schema:sameAs https://app.dimensions.ai/details/patent/EP-2286347-A2
24 schema:sdDatePublished 2019-04-18T10:06
25 schema:sdLicense https://scigraph.springernature.com/explorer/license/
26 schema:sdPublisher N2d4c6f87654a42b18ea4a8a90ed1d81d
27 sgo:license sg:explorer/license/
28 sgo:sdDataset patents
29 rdf:type sgo:Patent
30 N2d4c6f87654a42b18ea4a8a90ed1d81d schema:name Springer Nature - SN SciGraph project
31 rdf:type schema:Organization
32 N9674758eeed34577a753d7855a5564f9 schema:name CADAMBI, SRIHARI
33 rdf:type schema:Person
34 Nd8d3e8b3753b44798f8c0cc8d4f1499f rdf:first N9674758eeed34577a753d7855a5564f9
35 rdf:rest rdf:nil
36 anzsrc-for:2746 schema:inDefinedTermSet anzsrc-for:
37 rdf:type schema:DefinedTerm
38 sg:pub.10.1007/978-3-540-73400-0_33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004763848
39 https://doi.org/10.1007/978-3-540-73400-0_33
40 rdf:type schema:CreativeWork
41 https://doi.org/10.1109/dicta.2005.48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095240687
42 rdf:type schema:CreativeWork
43 https://doi.org/10.1109/icdar.2005.251 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095431167
44 rdf:type schema:CreativeWork
45 https://doi.org/10.1109/imtc.2004.1351489 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093689898
46 rdf:type schema:CreativeWork
47 https://doi.org/10.1109/vlsid.2007.73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095691174
48 rdf:type schema:CreativeWork
49 https://doi.org/10.1109/wises.2006.329117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095495462
50 rdf:type schema:CreativeWork
51 https://www.grid.ac/institutes/grid.419859.8 schema:Organization
 




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


...