{"ID":2836615,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21910","arxiv_id":"2511.21910","title":"Platinum: Path-Adaptable LUT-Based Accelerator Tailored for Low-Bit Weight Matrix Multiplication","abstract":"The rapid scaling of large language models demands more efficient hardware. Quantization offers a promising trade-off between efficiency and performance. With ultra-low-bit quantization, there are abundant opportunities for results reuse, and thus it can be boosted with lookup tables (LUTs) based acceleration. However, existing LUT-based methods suffer from computation and hardware overheads for LUT construction, and rely solely on bit-serial computation, which is suboptimal for ternary-weight networks. We propose Platinum, a lightweight ASIC accelerator for integer weight mixed-precision matrix multiplication (mpGEMM) using LUTs. Platinum reduces LUT construction overhead via offline-generated construction paths and supports both general bit-serial and optimized ternary-weight execution through adaptive path switching. On BitNet b1.58-3B, Platinum achieves up to 73.6x, 4.09x, and 2.15x speedups over SpikingEyeriss, Prosperity, and 16-thread T-MAC (CPU), respectively, along with energy reductions of 32.4x, 3.23x, and 20.9x, all within a 0.96mm2 chip area. This demonstrates the potential of LUT-based ASICs as efficient, scalable solutions for ultra-low-bit neural networks on edge platforms.","short_abstract":"The rapid scaling of large language models demands more efficient hardware. Quantization offers a promising trade-off between efficiency and performance. With ultra-low-bit quantization, there are abundant opportunities for results reuse, and thus it can be boosted with lookup tables (LUTs) based acceleration. However,...","url_abs":"https://arxiv.org/abs/2511.21910","url_pdf":"https://arxiv.org/pdf/2511.21910v1","authors":"[\"Haoxuan Shan\",\"Cong Guo\",\"Chiyue Wei\",\"Feng Cheng\",\"Junyao Zhang\",\"Hai \\\"Helen\\\" Li\",\"Yiran Chen\"]","published":"2025-11-26T20:53:14Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[\"Language Model\"]","has_code":false}