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arXiv · 2005.05968

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

Ranggi Hwang·Taehun Kim·Youngeun Kwon·Minsoo Rhu
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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.

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Ranggi Hwang, Taehun Kim, Youngeun Kwon, Minsoo Rhu. 2020-05-12. Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations. https://arxiv.org/abs/2005.05968

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