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

Autor:	Kim, Youngsuk, Lim, Junghwan, Lee, Hyuk-Jae, Rhee, Chae Eun
Rok vydání:	2024
Předmět:	Computer Science - Hardware Architecture Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.04032 Zobrazit plný text záznamu View this record from Arxiv