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

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. This paper presents PALUTE, a LUT-based Processing-In-Memory accelerator built on Monolithic 3D DRAM for efficient edge LLM inference. PALUTE enables in-DRAM LUT queries that exploit the vertical organization of M3D DRAM memory array tiles to achieve high parallelism with low area overhead. A near-memory LUT generator supports low-latency LUT generation for both GEMM and element-wise unary nonlinear operators, while a system-level tiering and scheduling strategy minimizes data movement across memory tiers. Evaluation using cycle-accurate simulation and RTL synthesis shows that PALUTE achieves 1,264 TPS end-to-end throughput at 0.16 W, improving energy efficiency by 12.8$\times$ over CHIME and 1.6$\times$ over FIGLUT, improving area efficiency by 2.0$\times$ over PIMPAL under W4A4 across Qwen3-4B models.