3 papers
cs.CL2026
Speculative Verification: Exploiting Information Gain to Refine Speculative Decoding
Sungkyun Kim, Jaemin Kim, Dogyung Yoon +3
LLMs have low GPU efficiency and high latency due to autoregressive decoding. Speculative decoding (SD) mitigates this using a small draft model to speculatively generate multiple…
cs.LG2026
Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate
Jaemin Kim, Sungkyun Kim, Junyeol Lee +1
Large Language Models (LLMs) are widely used across many domains, but their scale makes deployment challenging. Post-Training Quantization (PTQ) reduces memory footprint without re…
cs.LG2025
FlexiQ: Adaptive Mixed-Precision Quantization for Latency/Accuracy Trade-Offs in Deep Neural Networks
Jaemin Kim, Hongjun Um, Sungkyun Kim +2
Neural networks commonly execute on hardware accelerators such as NPUs and GPUs for their size and computation overhead. These accelerators are costly and it is hard to scale their…