collaborative filtering

hardware accelerators

users items

recommendation system

dnn_62253

architectures

memory_39386

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 $\mathrm{C}\mathrm{P}\mathrm{U}+$FPGA device, which shows a $ 1.7-17.2\times$ performance speedup and $1.7-19.5\times \mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}$-efficiency improvement than conventional approaches.

Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations