16 citations · 54 across the 20 of their papers we have counts for
20 papers
Faith: An Efficient Framework for Transformer Verification on GPUs
Boyuan Feng, Tianqi Tang, Yuke Wang +5
Transformer verification draws increasing attention in machine learning research and industry. It formally verifies the robustness of transformers against adversarial attacks such…
MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms
Yuke Wang, Boyuan Feng, Zheng Wang +4
The increasing size of input graphs for graph neural networks (GNNs) highlights the demand for using multi-GPU platforms. However, existing multi-GPU GNN systems optimize the compu…
GMI-DRL: Empowering Multi-GPU Deep Reinforcement Learning with GPU Spatial Multiplexing
Yuke Wang, Boyuan Feng, Zheng Wang +3
With the increasing popularity of robotics in industrial control and autonomous driving, deep reinforcement learning (DRL) raises the attention of various fields. However, DRL comp…
QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core
Yuke Wang, Boyuan Feng, Yufei Ding
Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhea…
On Adversarial Robustness of Point Cloud Semantic Segmentation
Jiacen Xu, Zhe Zhou, Boyuan Feng +2
Recent research efforts on 3D point cloud semantic segmentation (PCSS) have achieved outstanding performance by adopting neural networks. However, the robustness of these complex m…
TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
Yuke Wang, Boyuan Feng, Zheng Wang +2
Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance o…
Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
Anbang Wu, Gushu Li, Yuke Wang +3
Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers. However, training such variational quantum algor…
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Boyuan Feng, Yuke Wang, Tong Geng +2
Over the years, accelerating neural networks with quantization has been widely studied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit weights and 2-bit activati…
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions
Yuke Wang, Boyuan Feng, Yufei Ding
As the key advancement of the convolutional neural networks (CNNs), depthwise separable convolutions (DSCs) are becoming one of the most popular techniques to reduce the computatio…
Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks
Boyuan Feng, Yuke Wang, Zheng Wang +1
With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs. However, unlike the conventional…
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers
Boyuan Feng, Yuke Wang, Xu Li +1
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 autonomou…
An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks
Yuke Wang, Boyuan Feng, Xueqiao Peng +1
With the increasing popularity of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in various domains, such as image classification and object detection…
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
Boyuan Feng, Yuke Wang, Xu Li +3
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. Howe…
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Yuke Wang, Boyuan Feng, Gushu Li +4
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). However, the…
AccD: A Compiler-based Framework for Accelerating Distance-related Algorithms on CPU-FPGA Platforms
Yuke Wang, Boyuan Feng, Gushu Li +3
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 num…
KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA
Yuke Wang, Zhaorui Zeng, Boyuan Feng +2
K-means is a popular but computation-intensive algorithm for unsupervised learning. To address this issue, we present KPynq, a work-efficient triangle-inequality based K-means on F…
Penetrating the Fog: the Path to Efficient CNN Models
Kun Wan, Boyuan Feng, Shu Yang +1
With the increasing demand to deploy convolutional neural networks (CNNs) on mobile platforms, the sparse kernel approach was proposed, which could save more parameters than the st…
Domain-Adversarial Multi-Task Framework for Novel Therapeutic Property Prediction of Compounds
Lingwei Xie, Song He, Shu Yang +5
With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides huge opportunities to improve pharmaceutical research…
Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout
Kun Wan, Boyuan Feng, Lingwei Xie +1
Recently convolutional neural networks (CNNs) achieve great accuracy in visual recognition tasks. DenseNet becomes one of the most popular CNN models due to its effectiveness in fe…
SECS: Efficient Deep Stream Processing via Class Skew Dichotomy
Boyuan Feng, Kun Wan, Shu Yang +1
Despite that accelerating convolutional neural network (CNN) receives an increasing research focus, the save on resource consumption always comes with a decrease in accuracy. To bo…