b'Caiwen Ding'

research

∙ 08/22/2023

Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks

Graph Convolutional Networks (GCNs) are pivotal in extracting latent inf...

0 Xi Xie, et al. ∙

research

∙ 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...

0 Hongwu Peng, et al. ∙

research

∙ 08/16/2023

Towards Zero Memory Footprint Spiking Neural Network Training

Biologically-inspired Spiking Neural Networks (SNNs), processing informa...

0 Bin Lei, et al. ∙

research

∙ 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...

0 Bin Lei, et al. ∙

research

∙ 08/02/2023

Tango: rethinking quantization for graph neural network training on GPUs

Graph Neural Networks (GNNs) are becoming increasingly popular due to th...

0 Shiyang Chen, et al. ∙

research

∙ 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...

0 Ce Feng, et al. ∙

research

∙ 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 ...

0 Bin Lei, et al. ∙

research

∙ 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...

0 Hongwu Peng, et al. ∙

research

∙ 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...

0 Lijun Zhang, et al. ∙

research

∙ 04/24/2023

Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration

Biologically inspired Spiking Neural Networks (SNNs) have attracted sign...

23 Shaoyi Huang, et al. ∙

research

∙ 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...

0 Shanglin Zhou, et al. ∙

research

∙ 04/04/2023

Physics-aware Roughness Optimization for Diffractive Optical Neural Networks

As a representative next-generation device/circuit technology beyond CMO...

0 Shanglin Zhou, et al. ∙

research

∙ 03/25/2023

Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation

Object detection and multiple object tracking (MOT) are essential compon...

0 Sanbao Su, et al. ∙

research

∙ 02/08/2023

Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles

The recent advancements in wireless technology enable connected autonomo...

0 Songyang Han, et al. ∙

research

∙ 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...

0 Hongwu Peng, et al. ∙

research

∙ 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...

0 Yifan Gong, et al. ∙

research

∙ 11/30/2022

Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off

Over-parameterization of deep neural networks (DNNs) has shown high pred...

6 Shaoyi Huang, et al. ∙

research

∙ 11/26/2022

Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning

Recent advances in adversarial machine learning have shown that defenses...

0 Ethan Rathbun, et al. ∙

research

∙ 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...

5 Shengkun Tang, et al. ∙

research

∙ 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...

0 Bin Lei, et al. ∙

research

∙ 10/27/2022

Aerial Manipulation Using a Novel Unmanned Aerial Vehicle Cyber-Physical System

Unmanned Aerial Vehicles(UAVs) are attaining more and more maneuverabili...

0 Caiwu Ding, et al. ∙

research

∙ 10/08/2022

Towards Real-Time Temporal Graph Learning

In recent years, graph representation learning has gained significant po...

0 Deniz Gurevin, et al. ∙

research

∙ 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...

7 Hongwu Peng, et al. ∙

research

∙ 09/16/2022

Uncertainty Quantification of Collaborative Detection for Self-Driving

Sharing information between connected and autonomous vehicles (CAVs) fun...

0 Sanbao Su, et al. ∙

research

∙ 09/11/2022

Towards Sparsification of Graph Neural Networks

As real-world graphs expand in size, larger GNN models with billions of ...

0 Hongwu Peng, et al. ∙

research

∙ 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...

0 Nuo Xu, et al. ∙

research

∙ 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...

0 Hongwu Peng, et al. ∙

research

∙ 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...

0 Sahidul Islam, et al. ∙

research

∙ 06/21/2022

An Automatic and Efficient BERT Pruning for Edge AI Systems

With the yearning for deep learning democratization, there are increasin...

2 Shaoyi Huang, et al. ∙

research

∙ 01/28/2022

A Secure and Efficient Federated Learning Framework for NLP

In this work, we consider the problem of designing secure and efficient ...

0 Jieren Deng, et al. ∙

research

∙ 11/28/2021

Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices

Energy harvesting (EH) IoT devices that operate intermittently without b...

0 Sahidul Islam, et al. ∙

research

∙ 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...

0 Bingbing Li, et al. ∙

research

∙ 10/15/2021

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Various pruning approaches have been proposed to reduce the footprint re...

6 Shaoyi Huang, et al. ∙

research

∙ 09/16/2021

Dr. Top-k: Delegate-Centric Top-k on GPUs

Recent top-k computation efforts explore the possibility of revising var...

0 Anil Gaihre, et al. ∙

research

∙ 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...

0 Hongwu Peng, et al. ∙

research

∙ 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...

0 Zhepeng Wang, et al. ∙

research

∙ 08/10/2021

Binary Complex Neural Network Acceleration on FPGA

Being able to learn from complex data with phase information is imperati...

26 Hongwu Peng, et al. ∙

research

∙ 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...

4 Geng Yuan, et al. ∙

research

∙ 03/14/2021

TRUST: Triangle Counting Reloaded on GPUs

Triangle counting is a building block for a wide range of graph applicat...

0 Santosh Pandey, et al. ∙

research

∙ 03/11/2021

TAG: Transformer Attack from Gradient

Although federated learning has increasingly gained attention in terms o...

0 Jieren Deng, et al. ∙

research

∙ 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 ...

8 Yuhong Song, et al. ∙

research

∙ 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...

4 Deniz Gurevin, et al. ∙

research

∙ 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...

0 Bingbing Li, et al. ∙

research

∙ 09/14/2020

SAPAG: A Self-Adaptive Privacy Attack From Gradients

Distributed learning such as federated learning or collaborative learnin...

15 Yijue Wang, et al. ∙

research

∙ 09/03/2020

ESMFL: Efficient and Secure Models for Federated Learning

Deep Neural Networks are widely applied to various domains. The successf...

0 Sheng Lin, et al. ∙

research

∙ 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 ...

0 Yijue Wang, et al. ∙

research

∙ 07/16/2020

FTRANS: Energy-Efficient Acceleration of Transformers using FPGA

In natural language processing (NLP), the "Transformer" architecture was...

0 Bingbing Li, et al. ∙

research

∙ 04/12/2020

A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

To address the large model size and intensive computation requirement of...

13 Tianyun Zhang, et al. ∙

research

∙ 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...

43 Kaidi Xu, et al. ∙

research

∙ 11/24/2019

A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation

The computing wall and data movement challenges of deep neural networks ...

0 Geng Yuan, et al. ∙

Caiwen Ding

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