Publications (24)
ASAP: A Disaggregated and Asynchronous Inference System for MoE Prefill
Weiwei Chen, Shuang Chen, Lele Li +5
Mixture-of-Experts (MoE) models have become the de facto standard for scaling large language models. To maintain computational efficiency, modern MoE serving systems typically empl…
Harli: SLO-Aware Co-location of LLM Inference and PEFT-based Finetuning on Model-as-a-Service Platforms
Ao Xu, Han Zhao, Weihao Cui +7
Large language models (LLMs) are increasingly deployed under the Model-as-a-Service (MaaS) paradigm. To meet stringent quality-of-service (QoS) requirements, existing LLM serving s…
Meta-PINNs: Meta-Learning Enhanced Physics-Informed Machine Learning Framework for Turbomachinery Flow Predictions under Varying Operation Conditions
Yuling Han, Zhihui Li, Zhibin Yu
Coupling physics with machine learning models has shown great potential for solving fluid dynamics problems governed by partial differential equations. However, conventional method…
EncryptGAN: Image Steganography with Domain Transform
Ziqiang Zheng, Hongzhi Liu, Zhibin Yu +4
We propose an image steganographic algorithm called EncryptGAN, which disguises private image communication in an open communication channel. The insight is that content transform…
Discrete Codebook Design for Self-interference Suppression in mmWave ISAC
Guang Chai, Zhibin Yu, Thomas Wagner +2
This paper presents discrete codebook synthesis methods for self-interference (SI) suppression in a mmWave device, designed to support FD ISAC. We formulate a SINR maximization pro…
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network
Ziqiang Zheng, Chao Wang, Zhibin Yu +2
Semantic layouts based Image synthesizing, which has benefited from the success of Generative Adversarial Network (GAN), has drawn much attention in these days. How to enhance the…