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arXiv · 2601.13628

PRIMAL: Processing-In-Memory Based Low-Rank Adaptation for LLM Inference Accelerator

Yue Jiet Chong·Yimin Wang·Zhen Wu·Xuanyao Fong
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Abstract

This paper presents PRIMAL, a processing-in-memory (PIM) based large language model (LLM) inference accelerator with low-rank adaptation (LoRA). PRIMAL integrates heterogeneous PIM processing elements (PEs), interconnected by 2D-mesh inter-PE computational network (IPCN). A novel SRAM reprogramming and power gating (SRPG) scheme enables pipelined LoRA updates and sub-linear power scaling by overlapping reconfiguration with computation and gating idle resources. PRIMAL employs optimized spatial mapping and dataflow orchestration to minimize communication overhead, and achieves $1.5\times$ throughput and $25\times$ energy efficiency over NVIDIA H100 with LoRA rank 8 (Q,V) on Llama-13B.

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Yue Jiet Chong, Yimin Wang, Zhen Wu, Xuanyao Fong. 2026-01-20. PRIMAL: Processing-In-Memory Based Low-Rank Adaptation for LLM Inference Accelerator. https://doi.org/10.1109/iscas66217.2026.11561918

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