https://scholars.lib.ntu.edu.tw/handle/123456789/632316
標題: | FlashEmbedding: Storing embedding tables in SSD for large-scale recommender systems | 作者: | Wan H Sun X Cui Y CHIA-LIN YANG TEI-WEI KUO Xue C.J. |
關鍵字: | Embedding; Recommender systems; Solid-state drive (SSD) | 公開日期: | 2020 | 起(迄)頁: | 9月16日 | 來源出版物: | APSys 2021 - Proceedings of the 12th ACM SIGOPS Asia-Pacific Workshop on Systems | 摘要: | We present FlashEmbedding, a hardware/software co-design solution for storing embedding tables on SSDs for large-scale recommendation inference under memory capacity-limited systems. FlashEmbedding leverages an embedding semantic-aware SSD, an embedding-oriented software cache, and pipeline techniques to improve the overall performance. We evaluate the performance of FlashEmbedding with our FPGA-based prototype SSD on a real-world public dataset. FlashEmbedding achieves up to 17.44× lower latency in embedding lookups and 2.89× lower end-to-end latency than baseline solution in a memory capacity-limted system. © 2021 ACM. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118174515&doi=10.1145%2f3476886.3477511&partnerID=40&md5=04ac837135b7f22418e0b3e4b862ad08 https://scholars.lib.ntu.edu.tw/handle/123456789/632316 |
DOI: | 10.1145/3476886.3477511 | SDG/關鍵字: | Hardware-software codesign; Recommender systems; Semantics; Design solutions; Embeddings; Hardware/software codesign; Large-scales; Memory capacity; Performance; Semantic-aware; Software caches; Software pipeline; Solid-state drive; Embeddings |
顯示於: | 資訊工程學系 |
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