Efficient Cache Utilization via Model-aware Data Placement for Recommendation Models
Mohamed Assem Ibrahim, Onur Kayıran, Shaizeen Aga · 2021
Deep neural network (DNN) based recommendation models (RMs) represent a class of critical workloads that are broadly used in social media, entertainment content, and online businesses. Given their pervasive usage, understanding the memory subsystem behavior of these models is crucial, particularly from the perspective of future memory subsystem design. To this end, in this work, we first do an in-depth memory footprint and traffic analysis of emerging RMs. We observe that emerging RMs will severely stress future (and possibly larger) caches and memories.