  In today's era of "scale-out", this paper makes the case that a specialized
hardware architecture based on "scale-in"--placing as many specialized
processors as possible along with their memory systems and interconnect links
within one or two boards in a rack--would offer the potential to boost large
recommender system throughput by 12-62x for inference and 12-45x for training
compared to the DGX-2 state-of-the-art AI platform, while minimizing the
performance impact of distributing large models across multiple processors. By
analyzing Facebook's representative model--Deep Learning Recommendation Model
(DLRM)--from a hardware architecture perspective, we quantify the impact on
throughput of hardware parameters such as memory system design, collective
communications latency and bandwidth, and interconnect topology. By focusing on
conditions that stress hardware, our analysis reveals limitations of existing
AI accelerators and hardware platforms.
