Cost-effective Extension of DRAM-PIM for Group-wise LLM Quantization

Byeori Kim, Changhun Lee, Gwangsun Kim, Eunhyeok Park

Abstract: Processing-in-Memory (PIM) is emerging as a promising next-generation hardware to address memory bottlenecks in large language model (LLM) inference by leveraging internal memory bandwidth, enabling more energy-efficient on-device AI. However, LLMs’ large footprint poses significant challenges for accelerating them on PIM due to limited available space. Recent advances in weight-only quantization, especially group-wise weight quantization (GWQ), reduce LLM model sizes, enabling parameters to be stored at 4-bit precision or lower with minimal accuracy loss. Despite this, current PIM architectures experience performance degradation when handling the additional computations required for quantized weights. While incorporating extra logic could mitigate this degradation, it is often prohibitively expensive due to the constraints of memory technology, necessitating solutions with minimal area overhead. This work introduces two key innovations: 1) scale cascading, and 2) an INT2FP converter, to support GWQ-applied LLMs on PIM with minimal dequantization latency and area overhead compared to FP16 GEMV. Experimental results show that the proposed approach adds less than 0.6% area overhead to the existing PIM unit and achieves a 7% latency overhead for dequantization and GEMV in 4-bit GWQ with a group size of 128, compared to FP16 GEMV, while offering a 1.55× performance gain over baseline dequantization.