2 citations · 2 across the 5 of their papers we have counts for
8 papers
GPU-Accelerated Host-Aware Dead-Measurement Detection in Hybrid Quantum--Classical Programs: Full Version
Yanbin Chen, Qunyou Liu, Yu Wang +2
Hybrid programs combine a quantum circuit with a classical host program that consumes measurement outcomes. In such programs, an outcome may be syntactically read by the host but s…
Saving GPU Hours in LLM Inference System Development and Online Workloads with Simulation and DBMS-Inspired Cache Replacement Policies
Kyoungmin Kim, Jiacheng Li, Kijae Hong +3
LLMs are increasingly used world-wide from daily tasks to agentic systems and data analytics, requiring significant GPU resources. While LLM inference systems are capable of servin…
Mitigating the Bandwidth Wall via Data-Streaming System-Accelerator Co-Design
Qunyou Liu, Marina Zapater, David Atienza
Transformers have revolutionized AI in natural language processing and computer vision, but their large computation and memory demands pose major challenges for hardware accelerati…
SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
Qunyou Liu, Pengbo Yu, Marina Zapater +1
Deep neural networks (DNNs) are essential for performing advanced tasks on edge or mobile devices, yet their deployment is often hindered by severe resource constraints, including…
HAVEN: High-Bandwidth Flash Augmented Vector Engine for Large-Scale Approximate Nearest-Neighbor Search Acceleration
Po-Kai Hsu, Weihong Xu, Qunyou Liu +2
Retrieval-Augmented Generation (RAG) relies on large-scale Approximate Nearest Neighbor Search (ANNS) to retrieve semantically relevant context for large language models. Among ANN…
GreenLLM: SLO-Aware Dynamic Frequency Scaling for Energy-Efficient LLM Serving
Qunyou Liu, Darong Huang, Marina Zapater +1
Large Language Models (LLMs) are becoming the backbone of modern cloud services, yet their inference costs are dominated by GPU energy. Unlike traditional GPU workloads, LLM infere…