ProactivePIM: Accelerating Weight-Sharing Embedding Layer with PIM for Scalable Recommendation System

The model size growth of personalized recommendation systems poses new challenges for inference. Weight-sharing algorithms have been proposed for size reduction, but they increase memory access. Recent advancements in processing-in-memory (PIM) enhanced the model throughput by exploiting memory para...

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Hauptverfasser: Kim, Youngsuk, Lim, Junghwan, Lee, Hyuk-Jae, Rhee, Chae Eun
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Sprache:eng
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Zusammenfassung:The model size growth of personalized recommendation systems poses new challenges for inference. Weight-sharing algorithms have been proposed for size reduction, but they increase memory access. Recent advancements in processing-in-memory (PIM) enhanced the model throughput by exploiting memory parallelism, but such algorithms introduce massive CPU-PIM communication into prior PIM systems. We propose ProactivePIM, a PIM system for weight-sharing recommendation system acceleration. ProactivePIM integrates a cache within the PIM with a prefetching scheme to leverage a unique locality of the algorithm and eliminate communication overhead through a subtable mapping strategy. ProactivePIM achieves a 4.8x speedup compared to prior works.
DOI:10.48550/arxiv.2402.04032