SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations
Fuente:
arXiv
Saved in:
| Main Authors: | Guo, Wentao, Mishra, Mayank, Cheng, Xinle, Stoica, Ion, Dao, Tri |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
by: Mishra, Mayank, et al.
Published: (2026)
by: Mishra, Mayank, et al.
Published: (2026)
MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
by: Cao, Shiyi, et al.
Published: (2024)
by: Cao, Shiyi, et al.
Published: (2024)
MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
by: Huang, Zongle, et al.
Published: (2025)
by: Huang, Zongle, et al.
Published: (2025)
MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
by: Hannah, Lauren. A, et al.
Published: (2025)
by: Hannah, Lauren. A, et al.
Published: (2025)
GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
by: Wu, Haoze, et al.
Published: (2024)
by: Wu, Haoze, et al.
Published: (2024)
BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
by: Zhao, Jiayu, et al.
Published: (2026)
by: Zhao, Jiayu, et al.
Published: (2026)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
Grouter: Decoupling Routing from Representation for Accelerated MoE Training
by: Xu, Yuqi, et al.
Published: (2026)
by: Xu, Yuqi, et al.
Published: (2026)
DOT-MoE: Differentiable Optimal Transport for MoEfication
by: Bamba, Udbhav, et al.
Published: (2026)
by: Bamba, Udbhav, et al.
Published: (2026)
Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations
by: Hu, Wentao, et al.
Published: (2026)
by: Hu, Wentao, et al.
Published: (2026)
KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
by: Xu, Zukang, et al.
Published: (2026)
by: Xu, Zukang, et al.
Published: (2026)
GRIN: GRadient-INformed MoE
by: Liu, Liyuan, et al.
Published: (2024)
by: Liu, Liyuan, et al.
Published: (2024)
Accelerating MoE Model Inference with Expert Sharding
by: Balmau, Oana, et al.
Published: (2025)
by: Balmau, Oana, et al.
Published: (2025)
MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
by: Duanmu, Haojie, et al.
Published: (2025)
by: Duanmu, Haojie, et al.
Published: (2025)
Exploiting the Experts: Unauthorized Compression in MoE-LLMs
by: Neogi, Pinaki Prasad Guha, et al.
Published: (2025)
by: Neogi, Pinaki Prasad Guha, et al.
Published: (2025)
(GG) MoE vs. MLP on Tabular Data
by: Chernov, Andrei
Published: (2025)
by: Chernov, Andrei
Published: (2025)
SD-MoE: Spectral Decomposition for Effective Expert Specialization
by: Huang, Ruijun, et al.
Published: (2026)
by: Huang, Ruijun, et al.
Published: (2026)
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge
by: Hu, Gang, et al.
Published: (2025)
by: Hu, Gang, et al.
Published: (2025)
MoEITS: A Green AI approach for simplifying MoE-LLMs
by: Balderas, Luis, et al.
Published: (2026)
by: Balderas, Luis, et al.
Published: (2026)
MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
by: Li, Shuhuai, et al.
Published: (2026)
by: Li, Shuhuai, et al.
Published: (2026)
Mixture of Experts (MoE): A Big Data Perspective
by: Gan, Wensheng, et al.
Published: (2025)
by: Gan, Wensheng, et al.
Published: (2025)
Collaborative Compression for Large-Scale MoE Deployment on Edge
by: Chen, Yixiao, et al.
Published: (2025)
by: Chen, Yixiao, et al.
Published: (2025)
SDG-MoE: Signed Debate Graph Mixture-of-Experts
by: Kulibaba, Stepan, et al.
Published: (2026)
by: Kulibaba, Stepan, et al.
Published: (2026)
Expert Divergence Learning for MoE-based Language Models
by: Li, Jiaang, et al.
Published: (2026)
by: Li, Jiaang, et al.
Published: (2026)
Innovator: Scientific Continued Pretraining with Fine-grained MoE Upcycling
by: Liao, Ning, et al.
Published: (2025)
by: Liao, Ning, et al.
Published: (2025)
MoE-DisCo:Low Economy Cost Training Mixture-of-Experts Models
by: Ye, Xin, et al.
Published: (2026)
by: Ye, Xin, et al.
Published: (2026)
I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
by: Xin, Jiayi, et al.
Published: (2025)
by: Xin, Jiayi, et al.
Published: (2025)
LocMoE: A Low-Overhead MoE for Large Language Model Training
by: Li, Jing, et al.
Published: (2024)
by: Li, Jing, et al.
Published: (2024)
Ban&Pick: Ehancing Performance and Efficiency of MoE-LLMs via Smarter Routing
by: Chen, Yuanteng, et al.
Published: (2025)
by: Chen, Yuanteng, et al.
Published: (2025)
Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs
by: Zhang, Wuyue, et al.
Published: (2026)
by: Zhang, Wuyue, et al.
Published: (2026)
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
by: Chen, Yuanteng, et al.
Published: (2025)
by: Chen, Yuanteng, et al.
Published: (2025)
PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
by: Su, Yang, et al.
Published: (2025)
by: Su, Yang, et al.
Published: (2025)
STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
by: Wang, Yiming, et al.
Published: (2025)
by: Wang, Yiming, et al.
Published: (2025)
The Empirical Impact of Reducing Symmetries on the Performance of Deep Ensembles and MoE
by: Chernov, Andrei, et al.
Published: (2025)
by: Chernov, Andrei, et al.
Published: (2025)
Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis
by: Pei, Zehua, et al.
Published: (2025)
by: Pei, Zehua, et al.
Published: (2025)
Improving MoE Compute Efficiency by Composing Weight and Data Sparsity
by: Kilian, Maciej, et al.
Published: (2026)
by: Kilian, Maciej, et al.
Published: (2026)
Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training
by: Du, Xianzhi, et al.
Published: (2024)
by: Du, Xianzhi, et al.
Published: (2024)
Towards an empirical understanding of MoE design choices
by: Fan, Dongyang, et al.
Published: (2024)
by: Fan, Dongyang, et al.
Published: (2024)
SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models
by: Wu, Juntong, et al.
Published: (2026)
by: Wu, Juntong, et al.
Published: (2026)
Input Domain Aware MoE: Decoupling Routing Decisions from Task Optimization in Mixture of Experts
by: Hua, Yongxiang, et al.
Published: (2025)
by: Hua, Yongxiang, et al.
Published: (2025)
Similar Items
-
M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
by: Mishra, Mayank, et al.
Published: (2026) -
MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
by: Cao, Shiyi, et al.
Published: (2024) -
MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
by: Huang, Zongle, et al.
Published: (2025) -
MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
by: Hannah, Lauren. A, et al.
Published: (2025) -
GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
by: Wu, Haoze, et al.
Published: (2024)