TQIR: Trellis Quantization with Integer Reconstruction for Bandwidth-Efficient Edge LLM Acceleration

arXiv:2611.15231v1 Announce Type: new Abstract: Autoregressive large language model (LLM) inference is often limited by external-memory bandwidth, while existing ultra-low-bit trellis quantization reconstructs floating-point weights and leaves runtime Hadamard-transform and decoder costs unresolved. This paper presents TQIR, an algorithm--hardware co-design that decouples storage precision ($S$) from decoded-weight ($W$) and activation ($A$) compute precisions, enabling a better accuracy--hardware trade-off. TQIR performs trellis matching with floating-point candidates offline while constraining each candidate to an INT8 or INT4 codeword that can be reproduced exactly for integer GEMM at runtime.