activity
20182023
most citedQuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration

14 citations · 37 across the 10 of their papers we have counts for

collaborators

20 papers

cs.CV20231 cited

Stable Diffusion For Aerial Object Detection

Yanan Jian, Fuxun Yu, Simranjit Singh +1

Aerial object detection is a challenging task, in which one major obstacle lies in the limitations of large-scale data collection and the long-tail distribution of certain classes.…

cs.LG202214 cited

QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration

Zirui Xu, Fuxun Yu, Jinjun Xiong +1

The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries. On the contrary, although some work have proved that Quadrati…

cs.DC2022

A Survey of Multi-Tenant Deep Learning Inference on GPU

Fuxun Yu, Di Wang, Longfei Shangguan +3

Deep Learning (DL) models have achieved superior performance. Meanwhile, computing hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x throughput an…

cs.AR2021

Supporting Massive DLRM Inference Through Software Defined Memory

Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee +17

Deep Learning Recommendation Models (DLRM) are widespread, account for a considerable data center footprint, and grow by more than 1.5x per year. With model size soon to be in tera…

cs.AR20204 cited

Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination

Fuxun Yu, Zirui Xu, Tong Shen +12

Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the increasing computational cost makes them very challenging to meet real-time latency and accuracy requirements. A…

cs.AR2020

Third ArchEdge Workshop: Exploring the Design Space of Efficient Deep Neural Networks

Fuxun Yu, Dimitrios Stamoulis, Di Wang +2

This paper gives an overview of our ongoing work on the design space exploration of efficient deep neural networks (DNNs). Specifically, we cover two aspects: (1) static architectu…