HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference

2026-07-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

HCRMap is a novel hot expert residency mapping framework designed for pressure-aware expert replica management in 3.5D Mixture-of-Experts (MoE) chiplet inference. It addresses the persistent expert hotness skew observed in MoE large language models, where a small set of experts receives most tokens, causing compute imbalance and amplifying pressure on communication, memory bandwidth, I/O, and execution queues in multi-chiplet systems. HCRMap dynamically determines which experts to promote, retain, demote, or evict based on expert hotness, weight loading cost, migration overhead, and runtime resource pressure. By mapping routed token groups to suitable resident replicas, it jointly mitigates communication, memory, and queue bottlenecks. Experimental results demonstrate significant performance improvements, reducing end-to-end latency by 43.6% and 43.0% over Hydra in prefill and decode stages, respectively, by 34.5% and 33.1% over MoEntwine, and by 46.7% and 46.0% over PIMoE.

Key takeaway

For AI Scientists and Machine Learning Engineers optimizing MoE LLM inference on 3.5D multi-chiplet systems, HCRMap offers a significant latency reduction. You should consider implementing dynamic hot expert residency mapping to address expert hotness skew, which can drastically improve communication, memory, and queue performance. This approach, demonstrated to reduce latency by over 43% compared to existing methods, provides a clear path to more efficient and faster large language model deployments.

Key insights

HCRMap dynamically manages hot expert replicas in 3.5D MoE systems to mitigate resource bottlenecks caused by expert hotness skew.

Principles

• Expert hotness skews resource pressure.
• Dynamic replica management is key.
• Jointly optimize communication, memory, queues.

Method

HCRMap determines expert replica actions (promote, retain, demote, evict) by evaluating hotness, weight loading cost, migration overhead, and runtime resource pressure, then maps token groups.

In practice

• Optimize MoE LLM inference latency.
• Improve 3.5D multi-chiplet efficiency.
• Reduce communication bottlenecks.

Topics

• Mixture-of-Experts
• LLM Inference
• 3.5D Chiplet Systems
• Expert Residency Mapping
• Latency Optimization
• Resource Management

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.