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09/23/2022
Faith: An Efficient Framework for Transformer Verification on GPUs
Transformer verification draws increasing attention in machine learning ...
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09/14/2022
Empowering GNNs with Fine-grained Communication-Computation Pipelining on Multi-GPU Platforms
The increasing size of input graphs for graph neural networks (GNNs) hig...
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06/16/2022
GMI-DRL: Empowering Multi-GPU Deep Reinforcement Learning with GPU Spatial Multiplexing
With the increasing popularity of robotics in industrial control and aut...
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12/03/2021
TC-GNN: Accelerating Sparse Graph Neural Network Computation Via Dense Tensor Core on GPUs
Recently, graph neural networks (GNNs), as the backbone of graph-based m...
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11/26/2021
Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
Variational quantum algorithms are expected to demonstrate the advantage...
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11/18/2021
QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core
Over the most recent years, quantized graph neural network (QGNN) attrac...
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06/23/2021
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Over the years, accelerating neural networks with quantization has been ...
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01/04/2021
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions
As the key advancement of the convolutional neural networks (CNNs), dept...
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09/22/2020
Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks
With the increasing popularity of graph-based learning, graph neural net...
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09/22/2020
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers
Graph neural networks (GNNs) have achieved high performance in analyzing...
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09/11/2020
Optimizing Convolutional Neural Network Architecture via Information Field
CNN architecture design has attracted tremendous attention of improving ...
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07/09/2020
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
With the increasing popularity of graph-based learning, Graph Neural Net...
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06/11/2020
GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs
As the emerging trend of the graph-based deep learning, Graph Neural Net...
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08/26/2019
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 alg...
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05/22/2019
KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA
K-means is a popular but computation-intensive algorithm for unsupervise...
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10/09/2018
Penetrating the Fog: the Path to Efficient CNN Models
With the increasing demand to deploy convolutional neural networks (CNNs...
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09/28/2018
Domain-Adversarial Multi-Task Framework for Novel Therapeutic Property Prediction of Compounds
With the rapid development of high-throughput technologies, parallel acq...
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09/28/2018
Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout
Recently convolutional neural networks (CNNs) achieve great accuracy in ...
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09/07/2018