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10/29/2022
Enabling Data Movement and Computation Pipelining in Deep Learning Compiler
Pipelining between data loading and computation is a critical tensor pro...
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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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08/13/2022
CollComm: Enabling Efficient Collective Quantum Communication Based on EPR buffering
The noisy and lengthy nature of quantum communication hinders the develo...
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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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06/09/2022
Biologically Inspired Dynamic Thresholds for Spiking Neural Networks
The dynamic membrane potential threshold, as one of the essential proper...
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03/09/2022
Shfl-BW: Accelerating Deep Neural Network Inference with Tensor-Core Aware Weight Pruning
Weight pruning in deep neural networks (DNNs) can reduce storage and com...
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02/28/2022
Dynamic N:M Fine-grained Structured Sparse Attention Mechanism
Transformers are becoming the mainstream solutions for various tasks lik...
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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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11/26/2021
Mapping Surface Code to Superconducting Quantum Processors
In this paper, we formally describe the three challenges of mapping surf...
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11/26/2021
QECV: Quantum Error Correction Verification
Quantum Error Correction (QEC) is essential for fault-tolerant quantum c...
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11/25/2021
Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training
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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10/21/2021
Transformer Acceleration with Dynamic Sparse Attention
Transformers are the mainstream of NLP applications and are becoming inc...
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10/12/2021
LightSeq2: Accelerated Training for Transformer-based Models on GPUs
Transformer-based neural models are used in many AI applications. Traini...
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06/30/2021
Efficient Sparse Matrix Kernels based on Adaptive Workload-Balancing and Parallel-Reduction
Sparse matrix-vector and matrix-matrix multiplication (SpMV and SpMM) ar...
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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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03/11/2021
MPU: Towards Bandwidth-abundant SIMT Processor via Near-bank Computing
With the growing number of data-intensive workloads, GPU, which is the s...
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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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09/26/2020
Rubik: A Hierarchical Architecture for Efficient Graph Learning
Graph convolutional network (GCN) emerges as a promising direction to le...
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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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01/01/2020
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Recently, backpropagation through time inspired learning algorithms are ...
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11/28/2019
Poq: Projection-based Runtime Assertions for Debugging on a Quantum Computer
In this paper, we propose Poq, a runtime assertion scheme for debugging ...
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11/03/2019
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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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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03/10/2019
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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01/26/2019
Towards Weighted-Sampling Audio Adversarial Example Attack
Recent studies have highlighted audio adversarial examples as a ubiquito...
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01/26/2019
Adversarial attack on Speech-to-Text Recognition Models
Recent studies have highlighted audio adversarial examples as a ubiquito...
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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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10/01/2018
Dynamic Sparse Graph for Efficient Deep Learning
We propose to execute deep neural networks (DNNs) with dynamic and spars...
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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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09/22/2018
In-memory multiplication engine with SOT-MRAM based stochastic computing
Processing-in-memory (PIM) turns out to be a promising solution to break...
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09/07/2018
SECS: Efficient Deep Stream Processing via Class Skew Dichotomy
Despite that accelerating convolutional neural network (CNN) receives an...
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04/25/2018