Search Results for author:
Found 17 papers, 8 papers with code
Faith: An Efficient Framework for Transformer Verification on GPUs
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
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.
On Adversarial Robustness of Point Cloud Semantic Segmentation
As such, we present a comparative study of PCSS robustness.
TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
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
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
Over the years, accelerating neural networks with quantization has been widely studied.
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions
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.
Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks
With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs.
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers
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.
An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks
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.
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
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
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
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
PCNN: Environment Adaptive Model Without Finetuning
Convolutional Neural Networks (CNNs) have achieved tremendous success for many computer vision tasks, which shows a promising perspective of deploying CNNs on mobile platforms.
Penetrating the Fog: the Path to Efficient CNN Models
In this paper, we are the first in the field to consider how to craft an effective sparse kernel design by eliminating the large design space.
Domain-Adversarial Multi-Task Framework for Novel Therapeutic Property Prediction of Compounds
In this paper, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains.
Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout
The insights attained here could potentially be applied as a general approach for boosting the accuracy of other CNN models with similar nonlinear connections.
