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08/22/2023
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Graph Convolutional Networks (GCNs) are pivotal in extracting latent inf...
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08/20/2023
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highl...
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08/16/2023
Towards Zero Memory Footprint Spiking Neural Network Training
Biologically-inspired Spiking Neural Networks (SNNs), processing informa...
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08/16/2023
Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
Recent advancements in large-scale models, such as GPT-4, have showcased...
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08/02/2023
Tango: rethinking quantization for graph neural network training on GPUs
Graph Neural Networks (GNNs) are becoming increasingly popular due to th...
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07/25/2023
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering
Differential privacy is a widely accepted measure of privacy in the cont...
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07/15/2023
Creating a Dataset for High-Performance Computing Code Translation: A Bridge Between HPC Fortran and C++
In this study, we present a novel dataset for training machine learning ...
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06/27/2023
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Two-party computation (2PC) is promising to enable privacy-preserving de...
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05/20/2023
Dynamic Gradient Balancing for Enhanced Adversarial Attacks on Multi-Task Models
Multi-task learning (MTL) creates a single machine learning model called...
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04/24/2023
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Biologically inspired Spiking Neural Networks (SNNs) have attracted sign...
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04/08/2023
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning
Network pruning is a widely used technique to reduce computation cost an...
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04/04/2023
Physics-aware Roughness Optimization for Diffractive Optical Neural Networks
As a representative next-generation device/circuit technology beyond CMO...
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03/25/2023
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation
Object detection and multiple object tracking (MOT) are essential compon...
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02/08/2023
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
The recent advancements in wireless technology enable connected autonomo...
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02/05/2023
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of priv...
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12/09/2022
All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management
During the deployment of deep neural networks (DNNs) on edge devices, ma...
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11/30/2022
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
Over-parameterization of deep neural networks (DNNs) has shown high pred...
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11/26/2022
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
Recent advances in adversarial machine learning have shown that defenses...
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11/21/2022
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
Large-scale Transformer models bring significant improvements for variou...
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11/06/2022
Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach
Traffic state prediction in a transportation network is paramount for ef...
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10/27/2022
Aerial Manipulation Using a Novel Unmanned Aerial Vehicle Cyber-Physical System
Unmanned Aerial Vehicles(UAVs) are attaining more and more maneuverabili...
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10/08/2022
Towards Real-Time Temporal Graph Learning
In recent years, graph representation learning has gained significant po...
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09/20/2022
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emer...
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09/16/2022
Uncertainty Quantification of Collaborative Detection for Self-Driving
Sharing information between connected and autonomous vehicles (CAVs) fun...
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09/11/2022
Towards Sparsification of Graph Neural Networks
As real-world graphs expand in size, larger GNN models with billions of ...
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09/07/2022
Securing the Spike: On the Transferabilty and Security of Spiking Neural Networks to Adversarial Examples
Spiking neural networks (SNNs) have attracted much attention for their h...
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08/07/2022
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
Transformers are considered one of the most important deep learning mode...
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07/14/2022
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
IoT devices are increasingly being implemented with neural network model...
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06/21/2022
An Automatic and Efficient BERT Pruning for Edge AI Systems
With the yearning for deep learning democratization, there are increasin...
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01/28/2022
A Secure and Efficient Federated Learning Framework for NLP
In this work, we consider the problem of designing secure and efficient ...
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11/28/2021
Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices
Energy harvesting (EH) IoT devices that operate intermittently without b...
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10/15/2021
Detecting Gender Bias in Transformer-based Models: A Case Study on BERT
In this paper, we propose a novel gender bias detection method by utiliz...
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10/15/2021
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Various pruning approaches have been proposed to reduce the footprint re...
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09/16/2021
Dr. Top-k: Delegate-Centric Top-k on GPUs
Recent top-k computation efforts explore the possibility of revising var...
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09/13/2021
Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search
Molecular similarity search has been widely used in drug discovery to id...
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09/08/2021
Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs
With the constant increase of the number of quantum bits (qubits) in the...
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08/10/2021
Binary Complex Neural Network Acceleration on FPGA
Being able to learn from complex data with phase information is imperati...
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06/16/2021
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
Recent works demonstrated the promise of using resistive random access m...
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03/14/2021
TRUST: Triangle Counting Reloaded on GPUs
Triangle counting is a building block for a wide range of graph applicat...
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03/11/2021
TAG: Transformer Attack from Gradient
Although federated learning has increasingly gained attention in terms o...
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02/12/2021
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices
A pruning-based AutoML framework for run-time reconfigurability, namely ...
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12/18/2020
A Surrogate Lagrangian Relaxation-based Model Compression for Deep Neural Networks
Network pruning is a widely used technique to reduce computation cost an...
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09/17/2020
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
Pretrained large-scale language models have increasingly demonstrated hi...
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09/14/2020
SAPAG: A Self-Adaptive Privacy Attack From Gradients
Distributed learning such as federated learning or collaborative learnin...
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09/03/2020
ESMFL: Efficient and Secure Models for Federated Learning
Deep Neural Networks are widely applied to various domains. The successf...
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08/28/2020
MCMIA: Model Compression Against Membership Inference Attack in Deep Neural Networks
Deep learning or deep neural networks (DNNs) have nowadays enabled high ...
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07/16/2020
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
In natural language processing (NLP), the "Transformer" architecture was...
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04/12/2020
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
To address the large model size and intensive computation requirement of...
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02/25/2020
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several f...
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11/24/2019