MI-LLM: Multiplier-Free LLM Inference on Commodity Processing-in-Memory Hardware

Abstract

Large language models (LLMs) are prominent for their superior ability in language understanding and generation. However, a notorious problem for LLM inference is low computational utilization caused by the memory bottleneck, since it typically requires large memory capacity and high bandwidth to process neural weights. By integrating processing cores into memory, Processing-In-Memory (PIM) architecture excels at alleviating memory bottleneck; with the recent release of the first commodity near-bank PIM hardware (NBP), PIM becomes off-the-shelf and shows great potential for accelerating LLM inference practically. However, simply shoehorning LLM inference on NBP can not achieve satisfactory performance due to its inherent limitations: weak compute performance, frequent cache misses caused by the limited working memory capacity, and poor inter-PIM-core communication bandwidth. To address these limitations, we propose MI-LLM, an efficient system deploying LLM inference on NBP hardware. Its key idea is to build NBP-aware Lookup Tables (LUTs) and completely replace multiplications with lookups on LUTs, thereby mitigating the limitation of weak compute performance. 1) To reduce the model accuracy drop caused by the use of LUT, MI-LLM tailors a learning-based LUT construction method to maintain the model accuracy. 2) To cope with frequent cache misses caused by LUT sizes far exceeding PIM working memory capacity, MI-LLM introduces the design of PIM-aware linear kernel, with the optimization of intra-row and inter-row reordering enabled, to enhance LUT lookup locality. 3) MI-LLM further proposes a model partitioning scheme to minimize inter-PIM-core communication. Kernel-level benchmarks reveal that MI-LLM achieves a 9% throughput improvement and an 11% increase in energy efficiency over GPU implementations. Compared to FP8 quantization, MI-LLM incurs only a 0.24 times increase in perplexity, demonstrating minimal accuracy degradation. Moreover, in our end-to-end evaluation, MI-LLM requires 80% fewer ALU operation ticks per output token than the GPU baseline.