AI RESEARCH
Can We Trust LLMs on Memristors? Diving into Reasoning Ability under Non-Ideality
arXiv CS.CL
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ArXi:2603.13725v1 Announce Type: new Memristor-based analog compute-in-memory (CIM) architectures provide a promising substrate for the efficient deployment of Large Language Models (LLMs), owing to superior energy efficiency and computational density. However, these architectures suffer from precision issues caused by intrinsic non-idealities of memristors. In this paper, we first conduct a comprehensive investigation into the impact of such typical non-idealities on LLM reasoning.