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Robust Heterogeneous Analog-Digital Computing for Mixture-of-Experts Models with Theoretical Generalization Guarantees
arXiv – CS AI|Mohammed Nowaz Rabbani Chowdhury, Hsinyu Tsai, Geoffrey W. Burr, Kaoutar El Maghraoui, Liu Liu, Meng Wang||1 views
🤖AI Summary
Researchers propose a heterogeneous computing framework for Mixture-of-Experts AI models that combines analog in-memory computing with digital processing to improve energy efficiency. The approach identifies noise-sensitive experts for digital computation while running the majority on analog hardware, eliminating the need for costly retraining of large models.
Key Takeaways
- →New framework enables energy-efficient inference for large Mixture-of-Experts models without requiring expensive retraining.
- →Noise-sensitive experts are automatically identified by their maximum neuron norm and computed digitally for accuracy preservation.
- →Analog in-memory computing handles the majority of experts while digital processing manages critical components like attention layers.
- →Testing on DeepSeekMoE and OLMoE models shows maintained accuracy under analog hardware nonidealities.
- →Solution addresses memory and energy inefficiencies that plague current large-scale MoE model deployment.
#mixture-of-experts#analog-computing#energy-efficiency#ai-inference#hardware-optimization#machine-learning#neural-networks#computational-efficiency
Read Original →via arXiv – CS AI
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