@article {1434916,
	title = {RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference},
	journal = {ASPLOS 2021: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems},
	year = {2021},
	pages = {717{\textendash}729},
	abstract = {Neural personalized recommendationmodelsareusedacrossawide Samuel Hsia Harvard University Cambridge, Massachusetts, USA shsia@g.harvard.edu David Brooks Harvard University Cambridge, Massachusetts, USA dbrooks@eecs.harvard.edu USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3445814. 3446763 variety of datacenter applications including search, social media, and entertainment. State-of-the-art models comprise large embedding tables that have billions of parameters requiring large memory capacities. Unfortunately, large and fast DRAM-based memories levy high infrastructure costs. Conventional SSD-based storage solutions offer an order of magnitude larger capacity, but have worse read latency and bandwidth, degrading inference performance. RecSSD is a near data processing based SSD memory system customized for neural recommendation inference that reduces end-to-end model inference latency by 2{\texttimes} compared to using COTS SSDs across eight industry-representative models.},
	url = {https://doi.org/10.48550/arXiv.2102.00075},
	author = {Wilkening, Mark and Udit Gupta and Hsia, Samuel and Caroline Trippel and Carole-Jean Wu and David Brooks and Gu-Yeon Wei}
}
