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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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. |
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DOI: | 10.48550/arxiv.2402.04032 |