RecPIM: A PIM-Enabled DRAM-RRAM Hybrid Memory System For Recommendation Models

Heewoo Kim, Haojie Ye, Trevor N. Mudge, Ronald G. Dreslinski, Nishil Talati

Abstract: The performance of modern recommendation models is limited because of the memory bandwidth-hungry embedding layer reductions. We propose RecPIM-a novel hybrid memory system with DRAM and RRAM with PIM capability. The performance of traditional RRAM PIM is limited by the latency of bit-serial computation. RecPIM presents a comprehensive optimization approach that includes access-pattern-aware mapping, compute complexity reduction, and selective PIM reduction to offset this computation latency. Our evaluation shows that RecPIM offers significant performance, energy, and EDP improvement of 2.6×, 1.7×, and 4.4×, on average, compared to a CPU baseline. We also co-design wear-leveling techniques and demonstrate a practical lifetime of more than 12 years.