Anytime-Budget Embedding Allocation for DLRMs via Continuous-Capacity Multiple-Choice Knapsack
Embedding tables dominate the memory footprint of deep learning recommendation models (DLRMs), yet existing compression methods usually assume that the deployment memory budget is fixed before training. This is inconvenient in production, where the same recommender may be served across hardware tiers, multi-tenant reso...