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NORA: Noise-Optimized Rescaling of LLMs on Analog Compute-in-Memory Accelerators

Design, Automation, and Test in Europe(2025)

Rensselaer Polytechnic Institute

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Abstract
Large Language Models (LLMs) have become critical in AI applications, yet current digital AI accelerators suffer from significant energy inefficiencies due to frequent data movement. Analog compute-in-memory (CIM) accelerators offer a potential solution for improving energy efficiency but introduce non-idealities that can degrade LLM accuracy. While analog CIM has been extensively studied for traditional deep neural networks, its impact on LLMs remains unexplored, particularly concerning the large influence of Analog CIM non-idealities. In this paper, we conduct a sensitivity analysis on the effects of analog-induced noise on LLM accuracy. We find that while LLMs demonstrate robustness to weight-related noise, they are highly sensitive to quantization noise and additive Gaussian noise. Based on these insights, we propose a noise-optimized rescaling method to mitigate LLM accuracy loss by shifting the non-ideality burden from the sensitive input/output to the more resilient weight. Through rescaling, we can implement the OPT-6.7b model on simulated analog CIM hardware with less than 1% accuracy loss from the floating-point baseline, compared to a much higher loss of around 30% without rescaling.
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Large Language Models,Deep Neural Network,Gaussian Noise,Additive Noise,Additive Gaussian Noise,Load Data,Accuracy Loss,Artificial Intelligence Applications,Quantization Noise,Activation Function,Accuracy Of Model,Scaling Factor,Kurtosis,Matrix Multiplication,Vector Of Values,Output Current,Transformer Model,Memory Devices,Input Range,Non-volatile Memory,Non-volatile Memory Devices,Resistive Random Access Memory,Accuracy Drop,Ohm’s Law
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