ECE Distinguished Seminar



Robust and Efficient Deep Learning Systems at Edge



Dr. "Shelley" Xue Lin

Department of Electrical and Computer Engineering

Northeastern University



February 25, 2022, 11:00 am

RSCH 163

Zoom Link: https://gmu.zoom.us/j/93244528991


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Abstract

This talk presents recent work from Dr. Lin’s group on deep learning security and hardware acceleration. The first part is about vulnerability of deep neural networks. The second part is to implement efficient deep learning systems at edge covering both inference and training.



The talk begins with our design of structured adversarial examples, revealing structural information through strong group sparsity and providing better interpretability of the adversarial examples. Next, I will introduce our adversarial T-shirt, the first physical world adversarial example considering deformation of non-rigid objects. This work was published as a Spotlight Paper in ECCV’20 and has been broadly featured and cited in over 100 media outlets including Communications of the ACM, The Register, Boston Globe, etc. Then, I will present our recent work in ICLR’22 on reverse-engineering of adversarial perturbations to recover the original images. From hardware perspective, deep learning systems are subject to fault injection attacks, which manipulate neural network models for misclassification. I will discuss our modeling of such attacks through ADMM (alternating direction method of multipliers).



For efficient implementation techniques of deep learning at edge, I will briefly go through our paper leveraging model compression for mobile acceleration of 3D convolutional neural networks, achieving real-time execution for the first time. Then I will introduce our series of work on deep learning quantization for FPGA using intra-layer mixed schemes and multiple precisions. The talk ends with our NeurIPS’21 Spotlight Paper about sparse training using mobile devices.

Bio: Dr. "Shelley" Xue Lin is an assistant professor in the Department of Electrical and Computer Engineering at Northeastern University since 2017. She received her bachelor's degree from Tsinghua University in 2009 and PhD degree from the University of Southern California in 2016. Her research interests include deep learning security and hardware acceleration, machine learning and computing in cyber-physical systems, high-performance and mobile computing systems, and VLSI. She has received 12 research funding awards from government agencies such as NSF, ONR, Air Force Research Lab, DARPA, Lawrence Livermore National Lab, and U.S. Department of Transportation with a total amount of $8.5M and a personal share of $3.1M. Her research work has been recognized by 4 Top Paper Awards, 3 First Place Winners, and 3 Best Paper Awards. Her recently graduated PhD students Kaidi Xu and Pu Zhou became a tenure-track assistant professor at Drexel University and an assistant research professor at Northeastern University, respectively.