Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations
arXiv:2005.05968
Abstract
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.
Accepted for publication at the 47th IEEE/ACM International Symposium on Computer Architecture (ISCA-47), 2020
References in corpus (3)
- Deep Learning Recommendation Model for Personalization and Recommendation Systems
- Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
- Competitive Analysis System for Theatrical Movie Releases Based on Movie Trailer Deep Video Representation