Lam, MaximilianJohnson, JeffXiong, WenjieMaeng, KiwanGupta, UditLi, YangLai, LiangzhenLeontiadis, IliasRhu, MinsooLee, Hsien-Hsin S.Reddi, Vijay JanapaWei, Gu-YeonBrooks, DavidSuh, Edward2024-05-022024-05-022024-04-27https://hdl.handle.net/10919/118736On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1-10 GBs of data, making them impractical to store on-device. To overcome this barrier, we propose the use of private information retrieval (PIR) to efficiently and privately retrieve embeddings from servers without sharing any private information. As off-the-shelf PIR algorithms are usually too computationally intensive to directly use for latency-sensitive inference tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR with the downstream ML application to obtain further speedup. Our GPU acceleration strategy improves system throughput by more than 20× over an optimized CPU PIR implementation, and our PIR-ML co-design provides an over 5× additional throughput improvement at fixed model quality. Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to 100, 000 queries per second—a > 100× throughput improvement over a CPU-based baseline—while maintaining model accuracy.application/pdfenIn CopyrightArticle - Refereed2024-05-01The author(s)https://doi.org/10.1145/3617232.3624855