Pu Zhao

Hebei Academy of Agriculture and Forestry Sciences

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Yanzhi Wang

Northeastern University

Xue Lin

Universidad del Noreste

Wei Niu

Xi'an Aeronautical University

Bin Ren

William & Mary

Zheng Zhan

Northeastern University

Geng Yuan

University of Georgia

Qingwei Lin

Microsoft Research Asia (China)

Sijia Liu

China University of Petroleum, Beijing

Yifan Gong

Northeastern University

Zhenglun Kong

Northeastern University

Cooperative Institutions

Universidad del Noreste

Northeastern University

Chinese Academy of Sciences

Peking University

Tsinghua University

Chinese Academy of Agricultural Sciences

Microsoft Research (United Kingdom)

Hebei Academy of Agriculture and Forestry Sciences

Ministry of Agriculture and Rural Affairs

Microsoft Research Asia (China)

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Technique for Combined Dynamic Compression–Shear Testing of PBXs

Experimental Mechanics (2011)

Pu Zhao F. Lu Yuliang Lin Rui Chen J. L. Li

Split Hopkinson Pressure Bar

10.1007/s11340-011-9534-8

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Citations (31)

The correction for using X-cut quartz transducers in SHPB

DYMAT 2009 - 9th International Conferences on the Mechanical and Physical Behaviour of Materials under Dynamic Loading (2009)

Pu Zhao Yuliang Lin F. Lu Jian Li Rui Chen

10.1051/dymat/2009087

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Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks

Pu Zhao Siyue Wang Cheng Gongye Yanzhi Wang Yunsi Fei

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability. We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking attack with two constraints: 1) the classification of the other images should be unchanged and 2) the parameter modifications should be minimized. Specifically, the first constraint requires us not only to inject designated faults (misclassifications), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. The second constraint requires us to minimize the parameter modifications (using 0 norm to measure the number of modifications and 2 norm to measure the magnitude of modifications). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance.CCS CONCEPTS•Security and privacy → Domain-specific security and privacy architectures;Network security; •Networks → Network performance analysis; •Theory of computation → Theory and algorithms for application domains;

Deep Neural Networks

Vulnerability

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Citations (32)

Are existing customers worth more than new ones

Xintong Han Tong Wang Pu Zhao

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6.7ms on Mobile with over 78% ImageNet Accuracy: Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration.

arXiv (Cornell University) (2020)

Zhengang Li Geng Yuan Wei Niu Yanyu Li Pu Zhao

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. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed independently and do not fully consider compiler-level optimizations which is a must-do for mobile acceleration. In this work, we first propose (i) a general category of fine-grained structured pruning applicable to various DNN layers, and (ii) a comprehensive, compiler automatic code generation framework supporting different DNNs and different pruning schemes, which bridge the gap of model compression and NAS. We further propose NPAS, a compiler-aware unified network pruning, and architecture search. To deal with large search space, we propose a meta-modeling procedure based on reinforcement learning with fast evaluation and Bayesian optimization, ensuring the total number of training epochs comparable with representative NAS frameworks. Our framework achieves 6.7ms, 5.9ms, 3.9ms ImageNet inference times with 78.2%, 75% (MobileNet-V3 level), and 71% (MobileNet-V2 level) Top-1 accuracy respectively on an off-the-shelf mobile phone, consistently outperforming prior work.

Pruning

Mobile Edge Computing

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A Compression-Compilation Framework for On-mobile Real-time BERT Applications

arXiv (Cornell University) (2021)

Wei Niu Zhenglun Kong Geng Yuan Weiwen Jiang Jiexiong Guan

Transformer-based deep learning models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model to meet both resource and real-time specifications of mobile devices. Our framework applies a compiler-aware neural architecture optimization method (CANAO), which can generate the optimal compressed model that balances both accuracy and latency. We are able to achieve up to 7.8x speedup compared with TensorFlow-Lite with only minor accuracy loss. We present two types of BERT applications on mobile devices: Question Answering (QA) and Text Generation. Both can be executed in real-time with latency as low as 45ms. Videos for demonstrating the framework can be found on https://www.youtube.com/watch?v=_WIRvK_2PZI

Speedup

10.48550/arxiv.2106.00526

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Real-Time Execution of Large-scale Language Models on Mobile

arXiv (Cornell University) (2020)

Wei Niu Zhenglun Kong Geng Yuan Weiwen Jiang Jiexiong Guan

Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and computational speed on hardware platforms have impeded the popularity of pre-trained models, especially in the era of edge computing. In this paper, we seek to find the best model structure of BERT for a given computation size to match specific devices. We propose the first compiler-aware neural architecture optimization framework. Our framework can guarantee the identified model to meet both resource and real-time specifications of mobile devices, thus achieving real-time execution of large transformer-based models like BERT variants. We evaluate our model on several NLP tasks, achieving competitive results on well-known benchmarks with lower latency on mobile devices. Specifically, our model is 5.2x faster on CPU and 4.1x faster on GPU with 0.5-2% accuracy loss compared with BERT-base. Our overall framework achieves up to 7.8x speedup compared with TensorFlow-Lite with only minor accuracy loss.

10.48550/arxiv.2009.06823

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Citations (10)

Optimal Configuration of Island Microgrid Considering Wind/Photovoltaic/Line/Storage and Seawater Desalination Load

IOP Conference Series Earth and Environmental Science (2019)

Pu Zhao Qun Lin Haokai Xie Lezhen Pan Xudong Ji

Safe and reliable power supply is the basic guarantee for the development and construction of the island. Aimed at the problem of lack of electricity and water on the island, the paper proposes an optimal configuration method of island microgrid considering wind/photovoltaic/line/storage and seawater desalination loads. Firstly, the paper analyzes the load demand and the characteristics of renewable resources on the island, and proposes a grid-connected island microgrid system with multiple energy types and seawater desalination load. On this basis, an optimization model with the minimum annual cost as the objective function is established. The mixed integer linear programming(MILP) algorithm is used to optimize the capacity configuration and operation mode of the island microgrid system simultaneously. Finally, taking an island in the eastern coast of China as an example, the effectiveness of the optimal configuration method is verified through scenario analysis.

Microgrid

10.1088/1755-1315/330/5/052042

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MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation

arXiv (Cornell University) (2022)

Jiazhan Feng Qing-Feng Sun Can Xu Pu Zhao Yaming Yang

Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to better facilitate multi-modal conversation. MMDialog is composed of a curated set of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics. MMDialog has two main and unique advantages. First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x. Second, it contains massive topics to generalize the open-domain. To build engaging dialogue system with this dataset, we propose and normalize two response producing tasks based on retrieval and generative scenarios. In addition, we build two baselines for above tasks with state-of-the-art techniques and report their experimental performance. We also propose a novel evaluation metric MM-Relevance to measure the multi-modal responses. Our dataset and scripts are available in https://github.com/victorsungo/MMDialog.

Relevance

Open domain

10.48550/arxiv.2211.05719

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Study on the Influence of Dynamic Performance of Rail Pad on the Vibration Characteristics of E-Type Fastener Clip

Wang zhixuan Zhenxing He Weixing Liu Tianci Xu Chang Xu

Fastener

10.2139/ssrn.5144713

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