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New framework enhances MoE LLMs on noisy analog hardware

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ROMER, a post-training calibration framework designed to enhance the robustness of Mixture-of-Experts (MoE) Large Language Models (LLMs) when deployed on analog Compute-in-Memory (CIM) systems. This framework addresses hardware imperfections in CIM by replacing underutilized experts and recalibrating router decisions to maintain load balance and optimal routing under noisy conditions. Experiments show ROMER significantly reduces perplexity for models like DeepSeek-MoE, Qwen-MoE, and OLMoE when subjected to real-chip noise. AI

IMPACT Improves the viability of deploying LLMs on energy-efficient analog hardware by mitigating noise-induced performance degradation.

RANK_REASON The cluster contains an academic paper detailing a new method for improving LLM performance on specific hardware. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework enhances MoE LLMs on noisy analog hardware

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The cluster contains an academic paper detailing a new method for improving LLM performance on specific hardware. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ngai Wong ·

    ROMER: Expert Replacement and Router Calibration for Robust MoE LLMs on Analog Compute-in-Memory Systems

    Large language models (LLMs) with mixture-of-experts (MoE) architectures achieve remarkable scalability by sparsely activating a subset of experts per token, yet their frequent expert switching creates memory bandwidth bottlenecks that compute-in-memory (CIM) architectures are we…

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