  Neural personalized recommendation models are used across a wide variety of
datacenter applications including search, social media, and entertainment.
State-of-the-art models comprise large embedding tables that have billions of
parameters requiring large memory capacities. Unfortunately, large and fast
DRAM-based memories levy high infrastructure costs. Conventional SSD-based
storage solutions offer an order of magnitude larger capacity, but have worse
read latency and bandwidth, degrading inference performance. RecSSD is a near
data processing based SSD memory system customized for neural recommendation
inference that reduces end-to-end model inference latency by 2X compared to
using COTS SSDs across eight industry-representative models.
