Multi-path neural network, resource allocation method and multi-path neural network analyzer
The invention provides a multi-path neural network, a resource allocation method and a multi-path neural network analyzer. According to the resource allocating method, computing resources may be optimally allocated for a multipath neural network using a multipath neural network analyzer that include...
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | POURGHASSEMI BEHNAM NAJAFABADI KI YANG SEOK LEE JOO HWAN |
description | The invention provides a multi-path neural network, a resource allocation method and a multi-path neural network analyzer. According to the resource allocating method, computing resources may be optimally allocated for a multipath neural network using a multipath neural network analyzer that includes an interface and a processing device. The interface receives a multipath neural network. The processing device generates the multipath neural network to include one or more layers of a critical path through the multipath neural network that are allocated a first allocation of computing resources that are available to execute the multipath neural network. The critical path limits throughput of the multipath neural network. The first allocation of computing resources reduces an execution time ofthe multipath neural network to be less than a baseline execution time of a second allocation of computing resources for the multipath neural network. The first allocation of computing resources fora first layer of the criti |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN111476344A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN111476344A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN111476344A3</originalsourceid><addsrcrecordid>eNrjZIjxLc0pydQtSCzJUMhLLS1KzAFSJeX5Rdk6CkWpxfmlRcmpCok5OfnJiSWZ-XkKuaklGfkpCol5KQq5uHQCZRNzKqtSi3gYWNMSc4pTeaE0N4Oim2uIs4duakF-fGpxQWJyKlBLvLOfoaGhibmZsYmJozExagD0pDxh</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Multi-path neural network, resource allocation method and multi-path neural network analyzer</title><source>esp@cenet</source><creator>POURGHASSEMI BEHNAM NAJAFABADI ; KI YANG SEOK ; LEE JOO HWAN</creator><creatorcontrib>POURGHASSEMI BEHNAM NAJAFABADI ; KI YANG SEOK ; LEE JOO HWAN</creatorcontrib><description>The invention provides a multi-path neural network, a resource allocation method and a multi-path neural network analyzer. According to the resource allocating method, computing resources may be optimally allocated for a multipath neural network using a multipath neural network analyzer that includes an interface and a processing device. The interface receives a multipath neural network. The processing device generates the multipath neural network to include one or more layers of a critical path through the multipath neural network that are allocated a first allocation of computing resources that are available to execute the multipath neural network. The critical path limits throughput of the multipath neural network. The first allocation of computing resources reduces an execution time ofthe multipath neural network to be less than a baseline execution time of a second allocation of computing resources for the multipath neural network. The first allocation of computing resources fora first layer of the criti</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200731&DB=EPODOC&CC=CN&NR=111476344A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200731&DB=EPODOC&CC=CN&NR=111476344A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>POURGHASSEMI BEHNAM NAJAFABADI</creatorcontrib><creatorcontrib>KI YANG SEOK</creatorcontrib><creatorcontrib>LEE JOO HWAN</creatorcontrib><title>Multi-path neural network, resource allocation method and multi-path neural network analyzer</title><description>The invention provides a multi-path neural network, a resource allocation method and a multi-path neural network analyzer. According to the resource allocating method, computing resources may be optimally allocated for a multipath neural network using a multipath neural network analyzer that includes an interface and a processing device. The interface receives a multipath neural network. The processing device generates the multipath neural network to include one or more layers of a critical path through the multipath neural network that are allocated a first allocation of computing resources that are available to execute the multipath neural network. The critical path limits throughput of the multipath neural network. The first allocation of computing resources reduces an execution time ofthe multipath neural network to be less than a baseline execution time of a second allocation of computing resources for the multipath neural network. The first allocation of computing resources fora first layer of the criti</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZIjxLc0pydQtSCzJUMhLLS1KzAFSJeX5Rdk6CkWpxfmlRcmpCok5OfnJiSWZ-XkKuaklGfkpCol5KQq5uHQCZRNzKqtSi3gYWNMSc4pTeaE0N4Oim2uIs4duakF-fGpxQWJyKlBLvLOfoaGhibmZsYmJozExagD0pDxh</recordid><startdate>20200731</startdate><enddate>20200731</enddate><creator>POURGHASSEMI BEHNAM NAJAFABADI</creator><creator>KI YANG SEOK</creator><creator>LEE JOO HWAN</creator><scope>EVB</scope></search><sort><creationdate>20200731</creationdate><title>Multi-path neural network, resource allocation method and multi-path neural network analyzer</title><author>POURGHASSEMI BEHNAM NAJAFABADI ; KI YANG SEOK ; LEE JOO HWAN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN111476344A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>POURGHASSEMI BEHNAM NAJAFABADI</creatorcontrib><creatorcontrib>KI YANG SEOK</creatorcontrib><creatorcontrib>LEE JOO HWAN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>POURGHASSEMI BEHNAM NAJAFABADI</au><au>KI YANG SEOK</au><au>LEE JOO HWAN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Multi-path neural network, resource allocation method and multi-path neural network analyzer</title><date>2020-07-31</date><risdate>2020</risdate><abstract>The invention provides a multi-path neural network, a resource allocation method and a multi-path neural network analyzer. According to the resource allocating method, computing resources may be optimally allocated for a multipath neural network using a multipath neural network analyzer that includes an interface and a processing device. The interface receives a multipath neural network. The processing device generates the multipath neural network to include one or more layers of a critical path through the multipath neural network that are allocated a first allocation of computing resources that are available to execute the multipath neural network. The critical path limits throughput of the multipath neural network. The first allocation of computing resources reduces an execution time ofthe multipath neural network to be less than a baseline execution time of a second allocation of computing resources for the multipath neural network. The first allocation of computing resources fora first layer of the criti</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN111476344A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
title | Multi-path neural network, resource allocation method and multi-path neural network analyzer |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T19%3A25%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=POURGHASSEMI%20BEHNAM%20NAJAFABADI&rft.date=2020-07-31&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN111476344A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |