
Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
Variational quantum algorithms are expected to demonstrate the advantage...
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Mapping Surface Code to Superconducting Quantum Processors
In this paper, we formally describe the three challenges of mapping surf...
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QECV: Quantum Error Correction Verification
Quantum Error Correction (QEC) is essential for faulttolerant quantum c...
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Mitigating NoiseInduced Gradient Vanishing in Variational Quantum Algorithm Training
Variational quantum algorithms are expected to demonstrate the advantage...
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QGTC: Accelerating Quantized GNN via GPU Tensor Core
Over the most recent years, quantized graph neural network (QGNN) attrac...
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Transformer Acceleration with Dynamic Sparse Attention
Transformers are the mainstream of NLP applications and are becoming inc...
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Efficient Sparse Matrix Kernels based on Adaptive WorkloadBalancing and ParallelReduction
Sparse matrixvector and matrixmatrix multiplication (SpMV and SpMM) ar...
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APNNTC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Over the years, accelerating neural networks with quantization has been ...
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MPU: Towards Bandwidthabundant SIMT Processor via Nearbank Computing
With the growing number of dataintensive workloads, GPU, which is the s...
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DSXplore: Optimizing Convolutional Neural Networks via SlidingChannel Convolutions
As the key advancement of the convolutional neural networks (CNNs), dept...
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Rubik: A Hierarchical Architecture for Efficient Graph Learning
Graph convolutional network (GCN) emerges as a promising direction to le...
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Uncertaintyaware Attention Graph Neural Network for Defending Adversarial Attacks
With the increasing popularity of graphbased learning, graph neural net...
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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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Optimizing Convolutional Neural Network Architecture via Information Field
CNN architecture design has attracted tremendous attention of improving ...
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SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
With the increasing popularity of graphbased learning, Graph Neural Net...
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GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs
As the emerging trend of the graphbased deep learning, Graph Neural Net...
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Exploring Adversarial Attack in Spiking Neural Networks with SpikeCompatible Gradient
Recently, backpropagation through time inspired learning algorithms are ...
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Poq: Projectionbased Runtime Assertions for Debugging on a Quantum Computer
In this paper, we propose Poq, a runtime assertion scheme for debugging ...
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Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Spiking neural network is an important family of models to emulate the b...
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AccD: A Compilerbased Framework for Accelerating Distancerelated Algorithms on CPUFPGA Platforms
As a promising solution to boost the performance of distancerelated alg...
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KPynq: A WorkEfficient TriangleInequality based Kmeans on FPGA
Kmeans is a popular but computationintensive algorithm for unsupervise...
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Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints
As neural networks continue their reach into nearly every aspect of soft...
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Towards WeightedSampling Audio Adversarial Example Attack
Recent studies have highlighted audio adversarial examples as a ubiquito...
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Adversarial attack on SpeechtoText Recognition Models
Recent studies have highlighted audio adversarial examples as a ubiquito...
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Penetrating the Fog: the Path to Efficient CNN Models
With the increasing demand to deploy convolutional neural networks (CNNs...
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Dynamic Sparse Graph for Efficient Deep Learning
We propose to execute deep neural networks (DNNs) with dynamic and spars...
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DomainAdversarial MultiTask Framework for Novel Therapeutic Property Prediction of Compounds
With the rapid development of highthroughput technologies, parallel acq...
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Reconciling FeatureReuse and Overfitting in DenseNet with Specialized Dropout
Recently convolutional neural networks (CNNs) achieve great accuracy in ...
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Inmemory multiplication engine with SOTMRAM based stochastic computing
Processinginmemory (PIM) turns out to be a promising solution to break...
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SECS: Efficient Deep Stream Processing via Class Skew Dichotomy
Despite that accelerating convolutional neural network (CNN) receives an...
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Challenges Towards Deploying Data Intensive Scientific Applications on Extreme Heterogeneity Supercomputers
Shrinking transistors, which powered the advancement of computing in the...
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Yufei Ding
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