Search Results for author: yuke wang

Found 16 papers, 6 papers with code

Boosting Deep Neural Network Efficiency with Dual-Module Inference

no code implementations ICML 2020 Liu Liu, Lei Deng, Zhaodong Chen, yuke wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie

Using Deep Neural Networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs.

The Study of Perceptual Training of Chinese Mandarin Tones for Monolingual Speakers of English Using Adaptive Computer Based Training Software

no code implementations24 Sep 2023 yuke wang

The study explored a new technique of phonetic tone training, which may have a positive impact on second language learning and tone training.

Faith: An Efficient Framework for Transformer Verification on GPUs

1 code implementation23 Sep 2022 Boyuan Feng, Tianqi Tang, yuke wang, Zhaodong Chen, Zheng Wang, Shu Yang, Yuan Xie, Yufei Ding

In this paper, we propose Faith, an efficient framework for transformer verification on GPUs.


MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms

1 code implementation14 Sep 2022 yuke wang, Boyuan Feng, Zheng Wang, Tong Geng, Kevin Barker, Ang Li, Yufei Ding

For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery.

Layout Design Management

TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs

2 code implementations3 Dec 2021 yuke wang, Boyuan Feng, Zheng Wang, Guyue Huang, Yufei Ding

Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e. g., e-commerce).


Towards Efficient Ansatz Architecture for Variational Quantum Algorithms

no code implementations26 Nov 2021 Anbang Wu, Gushu Li, yuke wang, Boyuan Feng, Yufei Ding, Yuan Xie

In this paper, we propose a novel training scheme to mitigate such noise-induced gradient vanishing.

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

1 code implementation23 Jun 2021 Boyuan Feng, yuke wang, Tong Geng, Ang Li, Yufei Ding

Over the years, accelerating neural networks with quantization has been widely studied.


DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions

1 code implementation4 Jan 2021 yuke wang, Boyuan Feng, Yufei Ding

It also brings profound impact to improve the applicability of the compute- and memory-intensive CNNs to a broad range of applications, such as mobile devices, which are generally short of computation power and memory.

Rubik: A Hierarchical Architecture for Efficient Graph Learning

no code implementations26 Sep 2020 Xiaobing Chen, yuke wang, Xinfeng Xie, Xing Hu, Abanti Basak, Ling Liang, Mingyu Yan, Lei Deng, Yufei Ding, Zidong Du, Yunji Chen, Yuan Xie

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs.

Hardware Architecture

Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers

no code implementations22 Sep 2020 Boyuan Feng, yuke wang, Xu Li, Yufei Ding

Graph neural networks (GNNs) have achieved high performance in analyzing graph-structured data and have been widely deployed in safety-critical areas, such as finance and autonomous driving.

Adversarial Attack Autonomous Driving

Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks

no code implementations22 Sep 2020 Boyuan Feng, Yuke Wang, Zheng Wang, Yufei Ding

With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs.

An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks

no code implementations11 Sep 2020 Yuke Wang, Boyuan Feng, Xueqiao Peng, Yufei Ding

To clear these hurdles, we propose 3D-Receptive Field (3DRF), an explainable and easy-to-compute metric, to estimate the quality of a CNN architecture and guide the search process of designs.

Image Classification object-detection +1

SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization

no code implementations9 Jul 2020 Boyuan Feng, yuke wang, Xu Li, Shu Yang, Xueqiao Peng, Yufei Ding

With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from the research and industry field because of their high accuracy.


GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs

1 code implementation11 Jun 2020 Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, Yufei Ding

As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings).

Distributed, Parallel, and Cluster Computing

AccD: A Compiler-based Framework for Accelerating Distance-related Algorithms on CPU-FPGA Platforms

no code implementations26 Aug 2019 Yuke Wang, Boyuan Feng, Gushu Li, Lei Deng, Yuan Xie, Yufei Ding

As a promising solution to boost the performance of distance-related algorithms (e. g., K-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with numerous challenges.

Distributed, Parallel, and Cluster Computing Programming Languages

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