APEX system boosts edge MoE inference efficiency with adaptive prefetching

Researchers have developed APEX, an adaptive expert prefetching system designed to improve the efficiency of Mixture of Experts (MoE) models on edge devices. MoE models are attractive for edge deployment due to their high capacity and selective parameter activation, but their performance is often bottlenecked by memory access for expert parameters. APEX utilizes a lightweight prefetch router and a learned confidence model to predict and load expert parameters proactively, overlapping this loading process with computation. This approach achieves over 99% overlap accuracy and can reduce per-token latency by up to 26% and improve energy-delay product by up to 41% in its correctness-preserving mode, while a stall-free mode offers further efficiency gains with minimal impact on accuracy. AI

IMPACT Enhances efficiency for edge AI deployments, potentially enabling more powerful MoE models on resource-constrained devices.

RANK_REASON The cluster is a research paper detailing a new system for optimizing AI model inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• APEX
• Confidence modeling with reliability: a systems approach to sustainable energy planning
• Edge
• energy-delay product (EDP)
• Mixture of Experts (MoE)
• Off-Chip Memory Encryption and Integrity Protection Based on AES-GCM in Embedded Systems
• prefetch router

• infra
• paper

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