117 citations · 151 across the 8 of their papers we have counts for
8 papers
NELSSA: A GPU-PNM Heterogeneous System for Mixed-Length LLM Serving via Length-based Request Placement
Sookyung Choi, Seungyong Lee, Kangkyu Park +11
Modern LLMs and their agentic applications are broadening the range of serving workloads, spanning context lengths from a few hundred tokens to hundreds of thousands. As these requ…
LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure
Jaehong Cho, Hyunmin Choi, Guseul Heo +1
Large language model (LLM) serving infrastructures are undergoing a shift toward heterogeneity and disaggregation. Modern deployments increasingly integrate diverse accelerators an…
Déjà Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse
Jinwoo Hwang, Daeun Kim, Sangyeop Lee +8
Recently, Video-Language Models (VideoLMs) have demonstrated remarkable capabilities, offering significant potential for flexible and powerful video query systems. These models typ…
Throughput-Oriented LLM Inference via KV-Activation Hybrid Caching with a Single GPU
Sanghyeon Lee, Hongbeen Kim, Soojin Hwang +3
Large language models (LLMs) with growing model sizes use many GPUs to meet memory capacity requirements, incurring substantial costs for token generation. To provide cost-effectiv…
LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale
Jaehong Cho, Minsu Kim, Hyunmin Choi +2
Recently, there has been an extensive research effort in building efficient large language model (LLM) inference serving systems. These efforts not only include innovations in the…
ONNXim: A Fast, Cycle-level Multi-core NPU Simulator
Hyungkyu Ham, Wonhyuk Yang, Yunseon Shin +5
As DNNs are widely adopted in various application domains while demanding increasingly higher compute and memory requirements, designing efficient and performant NPUs (Neural Proce…
NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing
Guseul Heo, Sangyeop Lee, Jaehong Cho +6
Modern transformer-based Large Language Models (LLMs) are constructed with a series of decoder blocks. Each block comprises three key components: (1) QKV generation, (2) multi-head…
Accelerating String-Key Learned Index Structures via Memoization-based Incremental Training
Minsu Kim, Jinwoo Hwang, Guseul Heo +3
Learned indexes use machine learning models to learn the mappings between keys and their corresponding positions in key-value indexes. These indexes use the mapping information as…