16 citations · 17 across the 4 of their papers we have counts for
5 papers
Cassandra: Enabling Reasoning LLMs at Edge via Self-Speculative Decoding
Soongyu Choi, Yuntae Kim, Muyoung Son +1
Speculative decoding has emerged as a promising lossless approach for accelerating Large Language Models (LLMs). As reasoning LLMs increasingly suffer from decode-stage overhead an…
CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution
Muyoung Son, Yi Chen, Seungjae Yoo +2
The Mixture-of-Experts (MoE) architecture improves computational efficiency via sparse expert activation, but throughput-oriented inference faces substantial GPU memory pressure du…
LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization
Seunghee Han, Soongyu Choi, Joo-Young Kim
Recent advances in Protein Structure Prediction Models (PPMs), such as AlphaFold2 and ESMFold, have revolutionized computational biology by achieving unprecedented accuracy in pred…
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization
Minsu Kim, Seongmin Hong, RyeoWook Ko +5
Modern Large Language Model serving system batches multiple requests to achieve high throughput, while batching attention operations is challenging, rendering memory bandwidth a cr…
LPU: A Latency-Optimized and Highly Scalable Processor for Large Language Model Inference
Seungjae Moon, Jung-Hoon Kim, Junsoo Kim +14
The explosive arrival of OpenAI's ChatGPT has fueled the globalization of large language model (LLM), which consists of billions of pretrained parameters that embodies the aspects…