Authors: Youngsuk Kim, Hyuk-Jae Lee, Chae Eun Rhee

Published on: February 06, 2024

Impact Score: 8.07

Arxiv code: Arxiv:2402.04032

Summary

• What is new: Introduces HEAM, a heterogeneous memory architecture combining 3D-stacked DRAM with DIMM specifically for accelerating recommendation systems using compositional embedding.
• Why this is important: Personalized recommendation systems in data centers face challenges with large memory capacity and high bandwidth needs, outgrowing traditional solutions.
• What the research proposes: A three-tier memory hierarchy designed for compositional embedding, featuring DIMM, 3D-stacked DRAM, and Processing-In-Memory (PIM) to enhance efficiency and reduce memory size requirements.
• Results: Achieved a 6.3 times speedup and 58.9% energy savings over traditional systems.

Technical Details

Technological frameworks used: HEAM (Heterogeneous memory architecture)

Models used: Compositional embedding for recommendation systems

Data used: Tested on large-scale recommendation model datasets

Potential Impact

Data center providers, cloud services, and companies specializing in personalized recommendation systems could greatly benefit or need to adapt.

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