9 citations · 18 across the 5 of their papers we have counts for
8 papers
Dynamic Network Adaptation at Inference
Daniel Mendoza, Caroline Trippel
Machine learning (ML) inference is a real-time workload that must comply with strict Service Level Objectives (SLOs), including latency and accuracy targets. Unfortunately, ensurin…
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation
Geet Sethi, Bilge Acun, Niket Agarwal +3
We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement technique for deep learning recommendation models (DLRMs). RecShard is designed based on two ke…
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors
Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim +9
As autonomous driving and augmented reality evolve, a practical concern is data privacy. In particular, these applications rely on localization based on user images. The widely ado…
RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference
Mark Wilkening, Udit Gupta, Samuel Hsia +4
Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment. State-of-the-art models compr…
Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption
Meghan Cowan, Deeksha Dangwal, Armin Alaghi +3
Homomorphic encryption (HE) is a privacy-preserving technique that enables computation directly on encrypted data. Despite its promise, HE has seen limited use due to performance o…
CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
Kiwan Maeng, Shivam Bharuka, Isabel Gao +8
The paper proposes and optimizes a partial recovery training system, CPR, for recommendation models. CPR relaxes the consistency requirement by enabling non-failed nodes to proceed…