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

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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.
Publisher
IEEE/ACM
Issue Date
2020-06-03
Language
English
Citation

2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)

URI
http://hdl.handle.net/10203/276213
Appears in Collection
EE-Conference Papers(학술회의논문)
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