7 citations · 7 across the 6 of their papers we have counts for
6 papers
SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference
Jaeseong Lee, Seung-won Hwang, Samyam Rajbhandari
Semi-structured 2:4 sparsity is widely supported by modern accelerators, providing up to a 2x theoretical speedup. However, its strict 50% sparsity constraint often causes non-negl…
OWL: Overcoming Window Length-Dependence in Speculative Decoding for Long-Context Inputs
Jaeseong Lee, seung-won hwang, Aurick Qiao +3
Speculative decoding promises faster inference for large language models (LLMs), yet existing methods fail to generalize to real-world settings. Benchmarks typically assume short c…
Gold-Switch: Training-Free Superposition of Slow- and Fast- Thinking LLMs
Jaeseong Lee, Dayoung Kwon, seung-won hwang
Large Reasoning Models (LRMs) excel in structured tasks by emulating deliberate human reasoning but often suffer from overthinking, degrading performance and wasting resources. One…
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding
Yeonjoon Jung, Jaeseong Lee, Seungtaek Choi +3
Recently, pre-trained language models (PLMs) have been increasingly adopted in spoken language understanding (SLU). However, automatic speech recognition (ASR) systems frequently p…
STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning
Jaeseong Lee, seung-won hwang, Aurick Qiao +3
Mixture-of-experts (MoEs) have been adopted for reducing inference costs by sparsely activating experts in Large language models (LLMs). Despite this reduction, the massive number…
S3NAS: Fast NPU-aware Neural Architecture Search Methodology
Jaeseong Lee, Duseok Kang, Soonhoi Ha
As the application area of convolutional neural networks (CNN) is growing in embedded devices, it becomes popular to use a hardware CNN accelerator, called neural processing unit (…