DuoPIM: RRAM–DRAM Hybrid PIM Acceleration for Flexible-Batch LLM Decoding
Transformer-based large language models (LLMs) primarily consist of weight-intensive fully connected (FC) layers and cache-dependent attention layers. While batching significantly enhances the throughput of FC layers, it paradoxically increases the cache demands of attention layers. This provides no performance benefit and creates substantial memory pressure. Consequently, existing graphics processing unit (GPU)-based LLM acceleration systems face throughput limitations from batch size constraints. Even when DRAM-based processing-in-memory (PIM) is employed to accelerate attention, the utilization remains extremely low under small batch sizes, which is unsuitable for low-batch scenarios. Fortunately, the emerging nonvolatile resistive random access memory (RRAM) technology offers batch size-insensitive acceleration for FC layers through highly parallel in situ computations by eliminating weight loading overhead. This insight leads us to propose a hybrid approach: RRAM for FC layers and DRAM PIM for attention layers to overcome batch size limitations. However, merely scaling existing RRAM architectures misaligned with LLMs’ computation and storage demands will result in prohibitive overheads. Meanwhile, existing DRAM-based PIMs suffer from poor resource utilization due to the computational pattern of attention layers. Implementing an effective scheduling strategy is equally crucial to harness the potential of the hybrid PIM system. To address these challenges, we present DuoPIM, a novel RRAM–DRAM hybrid PIM architecture optimized for LLM decoding. We introduce novel architectural innovations for both the RRAM and DRAM PIM components to address the challenges posed by LLMs. Specifically, we decouple RRAM’s storage and computing capabilities within a hierarchical architecture, implement minimal modifications to DRAM PIM to support online softmax, and devise dedicated strategies across multiple architectural levels to enhance overall resource utilization. Evaluations demonstrate DuoPIM’s ability to fully leverage computing capacity across various batch sizes.