79 citations · 445 across the 27 of their papers we have counts for
28 papers
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed
Hao Cheng, Pu Zhao, Yize Li +4
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full…
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization
Yanyu Li, Pu Zhao, Geng Yuan +3
Neural architecture search (NAS) and network pruning are widely studied efficient AI techniques, but not yet perfect. NAS performs exhaustive candidate architecture search, incurri…
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration
Yifan Gong, Geng Yuan, Zheng Zhan +9
Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices. However, prior pru…
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
Sung-En Chang, Yanyu Li, Mengshu Sun +2
This work targets the commonly used FPGA (field-programmable gate array) devices as the hardware platform for DNN edge computing. We focus on DNN quantization as the main model com…
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
Sung-En Chang, Yanyu Li, Mengshu Sun +4
This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the firs…
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Geng Yuan, Xiaolong Ma, Wei Niu +13
Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity
Wei Niu, Zhengang Li, Xiaolong Ma +6
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resou…
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search
Zheng Zhan, Yifan Gong, Pu Zhao +9
Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learni…
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search
Pu Zhao, Wei Niu, Geng Yuan +4
Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to diff…
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
Siyue Wang, Xiao Wang, Pin-Yu Chen +2
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-tr…
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations
Hao Cheng, Kaidi Xu, Chenan Wang +3
To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximi…
Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework
Pranay Sharma, Kaidi Xu, Sijia Liu +3
In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations. The pro…
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
Sung-En Chang, Yanyu Li, Mengshu Sun +5
Deep Neural Networks (DNNs) have achieved extraordinary performance in various application domains. To support diverse DNN models, efficient implementations of DNN inference on edg…
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device
Pu Zhao, Wei Niu, Geng Yuan +7
3D object detection is an important task, especially in the autonomous driving application domain. However, it is challenging to support the real-time performance with the limited…
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
Zhengang Li, Geng Yuan, Wei Niu +13
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Pri…
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Kaidi Xu, Huan Zhang, Shiqi Wang +4
Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which…
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework
Sung-En Chang, Yanyu Li, Mengshu Sun +4
With the tremendous success of deep learning, there exists imminent need to deploy deep learning models onto edge devices. To tackle the limited computing and storage resources in…
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
Wei Niu, Mengshu Sun, Zhengang Li +7
Mobile devices are becoming an important carrier for deep learning tasks, as they are being equipped with powerful, high-end mobile CPUs and GPUs. However, it is still a challengin…
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Pu Zhao, Pin-Yu Chen, Payel Das +2
Mode connectivity provides novel geometric insights on analyzing loss landscapes and enables building high-accuracy pathways between well-trained neural networks. In this work, we…
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
Xiangyi Chen, Sijia Liu, Kaidi Xu +4
The adaptive momentum method (AdaMM), which uses past gradients to update descent directions and learning rates simultaneously, has become one of the most popular first-order optim…
Adversarial T-shirt! Evading Person Detectors in A Physical World
Kaidi Xu, Gaoyuan Zhang, Sijia Liu +6
It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-called physical adversarial examples deceive DNN-based decisionmakers by attaching advers…
Reweighted Proximal Pruning for Large-Scale Language Representation
Fu-Ming Guo, Sijia Liu, Finlay S. Mungall +2
Recently, pre-trained language representation flourishes as the mainstay of the natural language understanding community, e.g., BERT. These pre-trained language representations can…
PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices
Xiaolong Ma, Fu-Ming Guo, Wei Niu +5
Model compression techniques on Deep Neural Network (DNN) have been widely acknowledged as an effective way to achieve acceleration on a variety of platforms, and DNN weight prunin…
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
Xiao Wang, Siyue Wang, Pin-Yu Chen +4
Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on…