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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 7·5 min read
ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs
TL;DR
ACE introduces a novel framework for adaptive expert skipping in Mixture-of-Experts (MoE) based large language models (LLMs), significantly reducing computation without sacrificing performance. It outperforms existing methods, achieving a 7.96% reduction in perplexity and a 4.15 percentage point increase in accuracy at a 50% expert skipping rate.
✦ Why It Matters
Implement ACE in your MoE-based LLMs to enhance efficiency and performance today.
Key Takeaways
- Adopt ACE for adaptive expert skipping in LLMs.
- Utilize GSP for estimating transformation capacity.
- Implement RCR to evaluate expert contributions effectively.
- Achieve significant performance improvements with reduced computation.
How It Works
ACE operates by combining two innovative components: the Global Spectral Proxy (GSP) and Router-Conditioned Refinement (RCR). GSP assesses the overall transformation capacity of the model using gate projections and scaling techniques, while RCR analyzes expert-specific responses based on router weights.
During inference, ACE skips experts deemed low-contribution by both components, ensuring that the top-performing expert is always included.