Can We Trust LLMs on Memristors? Diving into Reasoning Ability under Non-Ideality

Name: Can We Trust LLMs on Memristors? Diving into Reasoning Ability under Non-Ideality
Uploaded: 2026-10-06T14:01:42.021285+00:00
Duration: 1134 s
Description: Memristor-based AI hardware for Large Language Models suffers from noise due to device imperfections, which severely degrades reasoning ability, especially in math tasks. The research tested three training-free fixes: Thinking Mode, In-Context Learning (ICL), and Module Redundancy. It found that module redundancy is most effective for recovering performance and energy efficiency.

Memristor-based AI hardware for Large Language Models suffers from noise due to device imperfections, which severely degrades reasoning ability, especially in math tasks. The research tested three training-free fixes: Thinking Mode, In-Context Learning (ICL), and Module Redundancy. It found that module redundancy is most effective for recovering performance and energy efficiency.

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