{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:22:13Z","timestamp":1773807733785,"version":"3.50.1"},"reference-count":20,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Micro"],"published-print":{"date-parts":[[2020,9,1]]},"DOI":"10.1109\/mm.2020.3015188","type":"journal-article","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T21:46:27Z","timestamp":1596836787000},"page":"26-36","source":"Crossref","is-referenced-by-count":8,"title":["A Single-Shot Generalized Device Placement for Large Dataflow Graphs"],"prefix":"10.1109","volume":"40","author":[{"given":"Yanqi","family":"Zhou","sequence":"first","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudip","family":"Roy","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amirali","family":"Abdolrashidi","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel Lin-Kit","family":"Wong","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Ma","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiumin","family":"Xu","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azalia","family":"Mirhoseini","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Laudon","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1285"},{"key":"ref11","article-title":"Spotlight: Optimizing device placement for training deep neural networks","author":"sutskever","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref12","article-title":"GPipe: Efficient training of giant neural networks using pipeline parallelism","author":"huang","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref13","article-title":"Mesh-tensorflow: Deep learning for supercomputers","author":"shazeer","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref14","article-title":"Superposition of many models into one","author":"cheung","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref15","article-title":"Proximal policy optimization algorithms","author":"schulman","year":"2017"},{"key":"ref16","article-title":"Placeto: Learning generalizable device placement algorithms for distributed machine learning","author":"addanki","year":"0","journal-title":"Proc NuerIPS"},{"key":"ref17","article-title":"Improving deep generative modeling with applications","author":"dai","year":"2019"},{"key":"ref18","article-title":"Attention is all you need","author":"ashish","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359646"},{"key":"ref4","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref3","article-title":"A hierarchical model for device placement","author":"mirhoseini","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref6","article-title":"Deep learning scaling is predictable, empirically","author":"joel","year":"2017"},{"key":"ref5","article-title":"How powerful are graph neural networks?","author":"xu","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref8","article-title":"REGAL: Transfer learning for fast optimization of computation graphs","author":"paliwal","year":"2019","journal-title":"Knowl Discov Data"},{"key":"ref7","article-title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer","author":"noam","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref2","article-title":"Beyond data and model parallelism for deep neural networks","author":"jia","year":"0","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref1","article-title":"Device placement optimization with reinforcement learning","author":"mirhoseini","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref9","article-title":"Reinforced genetic algorithm learning for optimizing computation graphs","author":"paliwal","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref20","article-title":"Graph attention networks","author":"veli?kovi?","year":"0","journal-title":"Proc Int Conf Learn Representations"}],"container-title":["IEEE Micro"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/40\/9186208\/09162436.pdf?arnumber=9162436","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T20:01:33Z","timestamp":1651694493000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9162436\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,1]]},"references-count":20,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/mm.2020.3015188","relation":{},"ISSN":["0272-1732","1937-4143"],"issn-type":[{"value":"0272-1732","type":"print"},{"value":"1937-4143","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,1]]}}}