PALUTE

PALUTE: Processing-In-Memory Acceleration via Lookup Table for Edge LLM Inference

arXiv:2606.08891v1 Announce Type: new Abstract: Large language models are increasingly deployed on edge devices with tight power and area budgets. While mixed-precision GEMM reduces arithmetic complexity, quantized inference is often dominated by dequantization and nonlinear operators. Lookup Table (LUT)-based method mitigates these costs by precomputing outputs and replacing repeated arithmetic with table lookups, but existing designs incur significant capacity and lookup-latency overheads.