5 papers
Beyond Retraining-Free MoE Compression: A Cost-Normalized Study of Post-Compression Adjustment
Sieun Hyeon, Jaeyoung Do
Retraining-free MoE compression reduces deployment memory by pruning or merging experts, but often treats the compressed checkpoint as the final artifact. We argue that this view i…
Exploiting Structural Properties for Efficient Constraint-Aware HNSW Hyperparameter Tuning
Geon Choi, Hoeun Lee, Jaeyoung Do
Vector databases (VectorDBs) are a core component of modern retrieval systems, including Retrieval-Augmented Generation (RAG), where efficient Approximate Nearest Neighbor Search (…
Revisiting Filtered ANN Benchmarks: A Hardness-Controlled Benchmark Generator for Realistic Evaluation
Mintaek Lim, Dogeun Kim, Minwoo Kim +1
Filtered approximate nearest neighbor (FANN) search must satisfy both vector similarity and structured predicates, yet evaluations remain brittle because real hybrid workloads are…
Is Retraining-Free Enough? The Necessity of Router Calibration for Efficient MoE Compression
Sieun Hyeon, Jaeyoung Do
Mixture-of-Experts (MoE) models scale capacity efficiently, but their massive parameter footprint creates a deployment-time memory bottleneck. We organize retraining-free MoE compr…
MatKV: Trading Compute for Flash Storage in LLM Inference
Kun-Woo Shin, Jay H. Park, Moonwook Oh +3
We observe two major trends in LLM-based generative AI: (1) inference is becoming the dominant factor in terms of cost and power consumption, surpassing training, and (2) retrieval…