{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T01:13:45Z","timestamp":1768353225511,"version":"3.49.0"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000028","name":"Semiconductor Research Corporation","doi-asserted-by":"publisher","award":["2024-PK-3266-G"],"award-info":[{"award-number":["2024-PK-3266-G"]}],"id":[{"id":"10.13039\/100000028","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Comput. Arch. Lett."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1109\/lca.2025.3580562","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T13:39:28Z","timestamp":1750167568000},"page":"201-204","source":"Crossref","is-referenced-by-count":1,"title":["Stardust: <u>S<\/u>calable and <u>T<\/u>r<u>a<\/u>nsfe<u>r<\/u>able Workloa<u>d<\/u> Mapping for Large AI on M<u>u<\/u>lti-Chiplet Sy<u>st<\/u>ems"],"prefix":"10.1109","volume":"24","author":[{"given":"Wencheng","family":"Zou","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, George Washington University, Washington, DC, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0525-8817","authenticated-orcid":false,"given":"Feiyun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, George Washington University, Washington, DC, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8291-4292","authenticated-orcid":false,"given":"Nan","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, George Washington University, Washington, DC, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9040670"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358302"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3418498"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA52012.2021.00050"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA57654.2024.00022"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC55918.2022.00031"},{"key":"ref8","first-page":"370","article-title":"A transferable approach for partitioning machine learning models on multi-chip-modules","volume-title":"Proc. 5th Conf. Mach. Learn. Syst.","author":"Xie"},{"key":"ref9","article-title":"GDP: Generalized device placement for dataflow graphs","author":"Zhou","year":"2019"},{"key":"ref10","article-title":"Transferable graph optimizers for ML compilers","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-68279-0_8"},{"key":"ref12","first-page":"2430","article-title":"Device placement optimization with reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mirhoseini"},{"key":"ref13","article-title":"A hierarchical model for device placement","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Mirhoseini"},{"key":"ref14","first-page":"1662","article-title":"Spotlight: Optimizing device placement for training deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gao"},{"key":"ref15","first-page":"9993","article-title":"Post: Device placement with cross-entropy minimization and proximal policy optimization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Gao"},{"key":"ref16","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref17","article-title":"Graph clustering with graph neural networks","volume":"24","author":"Tsitsulin","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0601602103"},{"key":"ref19","first-page":"17327","article-title":"Neural topological ordering for computation graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Gagrani"},{"key":"ref20","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304049"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/isscc.2018.8310291"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.23919\/VLSIC.2019.8778056"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref25","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Radford"},{"key":"ref26","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref28","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu","year":"2019"},{"key":"ref29","article-title":"DeBERTa: Decoding-enhanced BERT with disentangled attention","author":"He","year":"2020"},{"key":"ref30","article-title":"Florence: A new foundation model for computer vision","author":"Yuan","year":"2021"},{"key":"ref31","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref32","article-title":"GPT-Neo: Large scale autoregressive language modeling with mesh-Tensorflow","author":"Black","year":"2021"},{"key":"ref33","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref34","article-title":"CoAtNet: Marrying convolution and attention for all data sizes","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Dai"},{"key":"ref35","article-title":"Judging LLM-as-a-judge with MT-bench and chatbot arena","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zheng"},{"key":"ref36","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"ref37","first-page":"8821","article-title":"Zero-shot text-to-image generation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ramesh"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01598"},{"key":"ref39","article-title":"Mistral 7B","author":"Jiang","year":"2023"},{"key":"ref40","first-page":"34892","article-title":"Visual instruction tuning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Liu"}],"container-title":["IEEE Computer Architecture Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10208\/11062520\/11039063.pdf?arnumber=11039063","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T17:41:57Z","timestamp":1752601317000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11039063\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":39,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/lca.2025.3580562","relation":{},"ISSN":["1556-6056","1556-6064","2473-2575"],"issn-type":[{"value":"1556-6056","type":"print"},{"value":"1556-6064","type":"electronic"},{"value":"2473-2575","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]}}}