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TRINE: a tree-based silicon photonic interposer network for energy-efficient 2.5D machine learning acceleration

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dc.contributor.authorTaheri, Ebadollah, author
dc.contributor.authorMahdian, Mohammad Amin, author
dc.contributor.authorPasricha, Sudeep, author
dc.contributor.authorNikdast, Mahdi, author
dc.contributor.authorACM, publisher
dc.date.accessioned2024-11-11T19:31:38Z
dc.date.available2024-11-11T19:31:38Z
dc.date.issued2023-10-28
dc.description.abstract2.5D chiplet systems have showcased low manufacturing costs and modular designs for machine learning (ML) acceleration. Nevertheless, communication challenges arise from chiplet interconnectivity and high-bandwidth demands among chiplets. To address these challenges, we present TRINE, a novel tree-based silicon photonic interposer network for energy-efficient ML acceleration. Leveraging silicon photonics and broadband optical switching, TRINE enables efficient inter-chiplet communication with reduced latency and improved energy efficiency. Considering several ML workloads, our simulation results demonstrate significant improvements in the average energy efficiency by 61.7% and 40% when comparing TRINE with two recently proposed silicon photonic interposer networks. By overcoming communication limitations in 2.5D ML accelerators, this work is a promising step towards advancing 2.5D photonic-based ML accelerator design.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationEbadollah Taheri, Mohammad Amin Mahdian, Sudeep Pasricha, Mahdi Nikdast. 2023. In TRINE: A Tree-Based Silicon Photonic Interposer Network for Energy-Efficient 2.5D Machine Learning Acceleration. NoCArc '23: Proceedings of the 16th International Workshop on Network on Chip Architectures. Pages 15-20. https://doi.org/10.1145/3610396.3618091
dc.identifier.doihttps://doi.org/10.1145/3610396.3618091
dc.identifier.urihttps://hdl.handle.net/10217/239522
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofPublications
dc.relation.ispartofACM DL Digital Library
dc.rights© Ebadollah Taheri, et al. | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in NoCArc '2023, https://dx.doi.org/10.1145/3610396.3618091.
dc.subjecthardware
dc.subjectphotonic and optical interconnect
dc.subjectnetwork on chip
dc.titleTRINE: a tree-based silicon photonic interposer network for energy-efficient 2.5D machine learning acceleration
dc.typeText

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