Seungcheol Park, Jeongin Bae, Beomseok Kwon, Minjun Kim, Byeongwook Kim, Se Jung Kwon, U Kang, Dongsoo Lee. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Weight-only quantization has emerged as a promising solution to the deployment challenges of large language models (LLMs). However, it necessitates FP-INT operations, which make implementation on general-purpose hardware like GPUs difficult. In this paper, we propose FIGLUT, an efficient look-up table (LUT)-based GEMM accelerator architecture. Instead of performing traditional arithmetic operations, FIGLUT retrieves precomputed values from an LUT based on weight patterns, significantly reducing the computational complexity. We also introduce a novel LUT design that addresses the limitations of conventional memory architectures. To further improve LUT-based operations, we propose a half-size LUT combined with a dedicated decoding and multiplexing unit. FIGLUT efficiently supports different bit precisions and quantization methods using a single fixed hardware configuration. For the same 3-bit weight precision, FIGLUT demonstrates 59% higher TOPS/W and 20% lower perplexity than state-of-the-art accelerator design. When targeting the same perplexity, FIGLUT achieves $98 \%$ higher TOPS/W by performing 2.4-bit operations.

Weight-only quantization is widely used to mitigate the memory-bound nature of LLM inference. Codebook-based methods extend this trend by achieving strong accuracy in the extremely low-bit regime (e.g., 2-bit). However, current kernels rely on dequantization, which repeatedly fetches centroids and reconstructs weights, incurring substantial latency and cache pressure. We present CodeGEMM, a codebook-centric GEMM kernel that replaces dequantization with precomputed inner products between centroids and activations stored in a lightweight Psumbook. At inference, code indices directly gather these partial sums, eliminating per-element lookups and reducing the on-chip footprint. The kernel supports the systematic exploration of latency–memory–accuracy trade-offs under a unified implementation. On Llama-3 models, CodeGEMM delivers 1.83x (8B) and 8.93x (70B) speedups in the 2-bit configuration compared to state-of-the-art codebook-based quantization at comparable accuracy and further improves computing efficiency and memory subsystem utilization.