  Personalized recommendations are the backbone machine learning (ML) algorithm
that powers several important application domains (e.g., ads, e-commerce, etc)
serviced from cloud datacenters. Sparse embedding layers are a crucial building
block in designing recommendations yet little attention has been paid in
properly accelerating this important ML algorithm. This paper first provides a
detailed workload characterization on personalized recommendations and
identifies two significant performance limiters: memory-intensive embedding
layers and compute-intensive multi-layer perceptron (MLP) layers. We then
present Centaur, a chiplet-based hybrid sparse-dense accelerator that addresses
both the memory throughput challenges of embedding layers and the compute
limitations of MLP layers. We implement and demonstrate our proposal on an
Intel HARPv2, a package-integrated CPU+FPGA device, which shows a 1.7-17.2x
performance speedup and 1.7-19.5x energy-efficiency improvement than
conventional approaches.
