ROMER cuts perplexity by up to 59% in noisy analog CIM environments for MoE LLMs via expert replacement and router recalibration calibrated on real-chip measurements.
arXiv preprint arXiv:2502.05370 , year =
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VisMMoE exploits visual-expert affinity via token pruning to achieve up to 2.68x faster VL-MoE inference on memory-constrained hardware while keeping accuracy competitive.
FaaSMoE treats MoE experts as on-demand FaaS functions with configurable granularity, using under one-third the resources of a full-model baseline under multi-tenant workloads.
PreScope combines a layer-aware activation predictor, cross-layer prefetch scheduling, and asynchronous I/O to deliver 141% higher throughput and 74.6% lower latency for MoE inference on legacy hardware.
citing papers explorer
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ROMER: Expert Replacement and Router Calibration for Robust MoE LLMs on Analog Compute-in-Memory Systems
ROMER cuts perplexity by up to 59% in noisy analog CIM environments for MoE LLMs via expert replacement and router recalibration calibrated on real-chip measurements.
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VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading
VisMMoE exploits visual-expert affinity via token pruning to achieve up to 2.68x faster VL-MoE inference on memory-constrained hardware while keeping accuracy competitive.
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FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving
FaaSMoE treats MoE experts as on-demand FaaS functions with configurable granularity, using under one-third the resources of a full-model baseline under multi-tenant workloads.
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LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers
PreScope combines a layer-aware activation predictor, cross-layer prefetch scheduling, and asynchronous I/O to deliver 141% higher throughput and 74.6% lower latency for MoE inference on legacy hardware.