57 citations · 130 across the 15 of their papers we have counts for
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cs.LG2022
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…
cs.LG2022★ 1 cited
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…
cs.LG2020★ 4 cited
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…