Deep learning-based recommendation models (DLRMs) are widely deployed in commercial applications to enhance user experience. However, the large and sparse embedding layers in these models impose substantial memory bandwidth bottlenecks due to high memory access costs and irregular access patterns, leading to increased inference time and energy consumption. While resistive random access memory (ReRAM) based crossbars offer a fast and energy-efficient solution through in-memory embedding reduction operations, naively mapping embeddings onto crossbar arrays leads to poor crossbar utilization and thus degrades performance. We present ReCross, an efficient ReRAM-based in-memory computing (IMC) scheme designed to minimize execution time and enhance energy efficiency in DLRM embedding reduction. ReCross co-optimizes embedding access patterns and ReRAM crossbar characteristics by intelligently grouping and mapping co-occurring embeddings, replicating frequently accessed embeddings across crossbars, and dynamically selecting in-memory processing operations using a newly designed dynamic switch ADC circuit that considers runtime energy trade-offs. Experimental results demonstrate that ReCross achieves a 3.97x reduction in execution time and a 6.1x improvement in energy efficiency compared to state-of-the-art IMC approaches.

翻译：基于深度学习的推荐模型（DLRM）被广泛部署于商业应用中，以提升用户体验。然而，这些模型中庞大且稀疏的嵌入层，由于高昂的内存访问成本和不规则的访问模式，带来了显著的内存带宽瓶颈，导致推理时间增加和能耗上升。虽然基于阻变随机存取存储器（ReRAM）的交叉阵列通过内存内嵌入缩减操作提供了一种快速且高能效的解决方案，但直接将嵌入映射到交叉阵列会导致交叉阵列利用率低下，从而降低性能。我们提出了ReCross，一种高效的基于ReRAM的内存计算（IMC）方案，旨在最小化DLRM嵌入缩减的执行时间并提升能效。ReCross通过智能地分组和映射共现嵌入、在交叉阵列间复制频繁访问的嵌入，以及利用新设计的动态开关ADC电路（该电路考虑了运行时能耗权衡）动态选择内存内处理操作，协同优化了嵌入访问模式和ReRAM交叉阵列特性。实验结果表明，与最先进的IMC方法相比，ReCross实现了执行时间减少3.97倍，能效提升6.1倍。