SCHEDULING CONFIGURATION FOR DEEP LEARNING NETWORKS
In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | 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 | Bar-On, Tomer Walter, Zigi Jacob, Guy Fais, Yaniv Hirsch, Shira Ben-Avi, Eran Faivishevsky, Lev Dreyfuss, Jeremie Zmora, Neta Weisel, Orly Oren, Yarden Subag, Jacob |
| description | In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2024419956A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2024419956A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2024419956A13</originalsourceid><addsrcrecordid>eNrjZDAOdvZwdQn18fRzV3D293PzdA8Ncgzx9PdTcPMPUnBxdQ1Q8HF1DPIDyfu5hoT7B3kH8zCwpiXmFKfyQmluBmU31xBnD93Ugvz41OKCxOTUvNSS-NBgIwMjExNDS0tTM0dDY-JUAQBVYShS</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>SCHEDULING CONFIGURATION FOR DEEP LEARNING NETWORKS</title><source>esp@cenet</source><creator>Bar-On, Tomer ; Walter, Zigi ; Jacob, Guy ; Fais, Yaniv ; Hirsch, Shira ; Ben-Avi, Eran ; Faivishevsky, Lev ; Dreyfuss, Jeremie ; Zmora, Neta ; Weisel, Orly ; Oren, Yarden ; Subag, Jacob</creator><creatorcontrib>Bar-On, Tomer ; Walter, Zigi ; Jacob, Guy ; Fais, Yaniv ; Hirsch, Shira ; Ben-Avi, Eran ; Faivishevsky, Lev ; Dreyfuss, Jeremie ; Zmora, Neta ; Weisel, Orly ; Oren, Yarden ; Subag, Jacob</creatorcontrib><description>In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2024</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=20241219&DB=EPODOC&CC=US&NR=2024419956A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25562,76317</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20241219&DB=EPODOC&CC=US&NR=2024419956A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Bar-On, Tomer</creatorcontrib><creatorcontrib>Walter, Zigi</creatorcontrib><creatorcontrib>Jacob, Guy</creatorcontrib><creatorcontrib>Fais, Yaniv</creatorcontrib><creatorcontrib>Hirsch, Shira</creatorcontrib><creatorcontrib>Ben-Avi, Eran</creatorcontrib><creatorcontrib>Faivishevsky, Lev</creatorcontrib><creatorcontrib>Dreyfuss, Jeremie</creatorcontrib><creatorcontrib>Zmora, Neta</creatorcontrib><creatorcontrib>Weisel, Orly</creatorcontrib><creatorcontrib>Oren, Yarden</creatorcontrib><creatorcontrib>Subag, Jacob</creatorcontrib><title>SCHEDULING CONFIGURATION FOR DEEP LEARNING NETWORKS</title><description>In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDAOdvZwdQn18fRzV3D293PzdA8Ncgzx9PdTcPMPUnBxdQ1Q8HF1DPIDyfu5hoT7B3kH8zCwpiXmFKfyQmluBmU31xBnD93Ugvz41OKCxOTUvNSS-NBgIwMjExNDS0tTM0dDY-JUAQBVYShS</recordid><startdate>20241219</startdate><enddate>20241219</enddate><creator>Bar-On, Tomer</creator><creator>Walter, Zigi</creator><creator>Jacob, Guy</creator><creator>Fais, Yaniv</creator><creator>Hirsch, Shira</creator><creator>Ben-Avi, Eran</creator><creator>Faivishevsky, Lev</creator><creator>Dreyfuss, Jeremie</creator><creator>Zmora, Neta</creator><creator>Weisel, Orly</creator><creator>Oren, Yarden</creator><creator>Subag, Jacob</creator><scope>EVB</scope></search><sort><creationdate>20241219</creationdate><title>SCHEDULING CONFIGURATION FOR DEEP LEARNING NETWORKS</title><author>Bar-On, Tomer ; Walter, Zigi ; Jacob, Guy ; Fais, Yaniv ; Hirsch, Shira ; Ben-Avi, Eran ; Faivishevsky, Lev ; Dreyfuss, Jeremie ; Zmora, Neta ; Weisel, Orly ; Oren, Yarden ; Subag, Jacob</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2024419956A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Bar-On, Tomer</creatorcontrib><creatorcontrib>Walter, Zigi</creatorcontrib><creatorcontrib>Jacob, Guy</creatorcontrib><creatorcontrib>Fais, Yaniv</creatorcontrib><creatorcontrib>Hirsch, Shira</creatorcontrib><creatorcontrib>Ben-Avi, Eran</creatorcontrib><creatorcontrib>Faivishevsky, Lev</creatorcontrib><creatorcontrib>Dreyfuss, Jeremie</creatorcontrib><creatorcontrib>Zmora, Neta</creatorcontrib><creatorcontrib>Weisel, Orly</creatorcontrib><creatorcontrib>Oren, Yarden</creatorcontrib><creatorcontrib>Subag, Jacob</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bar-On, Tomer</au><au>Walter, Zigi</au><au>Jacob, Guy</au><au>Fais, Yaniv</au><au>Hirsch, Shira</au><au>Ben-Avi, Eran</au><au>Faivishevsky, Lev</au><au>Dreyfuss, Jeremie</au><au>Zmora, Neta</au><au>Weisel, Orly</au><au>Oren, Yarden</au><au>Subag, Jacob</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>SCHEDULING CONFIGURATION FOR DEEP LEARNING NETWORKS</title><date>2024-12-19</date><risdate>2024</risdate><abstract>In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_epo_espacenet_US2024419956A1 |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | SCHEDULING CONFIGURATION FOR DEEP LEARNING NETWORKS |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T23%3A40%3A18IST&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=Bar-On,%20Tomer&rft.date=2024-12-19&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2024419956A1%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 |