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

arXiv: https://arxiv.org/abs/2607.11586 · HTML: https://mlsanity.com/paper/2607.11586/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Efficient MoE training and inference systems, one of 12 directions in Mixture-of-experts.
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Placed in

- Mixture-of-experts / [Efficient MoE training and inference systems](https://mlsanity.com/moe/efficient-moe-training-and-inference-systems/)

## Builds on (most cited first, 10 of 10)

- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://mlsanity.com/paper/2401.06066/) (2024)
- [GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference](https://mlsanity.com/paper/2509.25041/) (2025)
- [MoNDE: Mixture of Near-Data Experts for Large-Scale Sparse Models](https://mlsanity.com/paper/2405.18832/) (2024)
- [UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing](https://mlsanity.com/paper/2606.04101/) (2026)
- [PROBE: Co-Balancing Computation and Communication in MoE Inference via Real-Time Predictive Prefetching](https://mlsanity.com/paper/2602.00509/) (2026)
- [MoEntwine: Unleashing the Potential of Wafer-scale Chips for Large-scale Expert Parallel Inference](https://mlsanity.com/paper/2510.25258/) (2025)
- [CRAFT: Fine-Grained Cost-Aware Expert Replication For Efficient Mixture-of-Experts Serving](https://mlsanity.com/paper/2603.28768/) (2026)
- [LaMoSys3.5D: Enabling 3.5D-IC-Based Large Language Model Inference Serving Systems via Hardware/Software Co-Design](https://mlsanity.com/paper/2512.08731/) (2025)
- [Mozart: Modularized and Efficient MoE Training on 3.5D Wafer-Scale Chiplet Architectures](https://mlsanity.com/paper/2603.07006/) (2026)

## Abstract (arXiv)

Mixture-of-Experts (MoE) large language models (LLM) activate only a small number of experts during inference, but token routing introduces persistent expert hotness skew: a small set of hot experts continuously receives most tokens, while the remaining experts are lightly loaded. On 3.5D multi-chiplet systems, this skew not only causes compute imbalance but also amplifies pressure on communication, memory bandwidth, I/O, and execution queues. Therefore, the core problem is not simply to reduce token movement, but to dynamically place and reuse hot expert replicas across different memory tiers. This paper proposes HCRMap, a hot expert residency mapping framework for pressure-aware expert replica management in 3.5D MoE inference. Based on expert hotness, weight loading cost, migration overhead, and runtime resource pressure, HCRMap dynamically determines which experts should be promoted, retained, demoted, or evicted. It then maps routed token groups to suitable resident replicas, thereby jointly mitigating communication, memory, and queue bottlenecks. Experimental results show that HCRMap reduces end-to-end latency by 43.6% and 43.0% over Hydra in the prefill and decode stages, respectively; by 34.5% and 33.1% over MoEntwine; and by 46.7% and 46.0% over PIMoE.