1k citations · 1.4k across the 22 of their papers we have counts for
22 papers
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
Weilin Liu, Ye Mu, Chao Yu +6
Discovering hazardous scenarios is crucial in testing and further improving driving policies. However, conducting efficient driving policy testing faces two key challenges. On the…
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness
Yi Cai, Xuefei Ning, Huazhong Yang +1
Adversarial attacks have rendered high security risks on modern deep learning systems. Adversarial training can significantly enhance the robustness of neural network models by sup…
Enabling Lower-Power Charge-Domain Nonvolatile In-Memory Computing with Ferroelectric FETs
Guodong Yin, Yi Cai, Juejian Wu +6
Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications. Compared to current-domain CiM solutions, charge-domain CiM…
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach
Xuefei Ning, Junbo Zhao, Wenshuo Li +4
Convolutional neural networks (CNNs) are vulnerable to adversarial examples, and studies show that increasing the model capacity of an architecture topology (e.g., width expansion)…
aw_nas: A Modularized and Extensible NAS framework
Xuefei Ning, Changcheng Tang, Wenshuo Li +7
Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner. aw_nas is an open-source Py…
Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes
Feng Gao, Jincheng Yu, Hao Shen +2
Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of v…
BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures
Tianchen Zhao, Xuefei Ning, Xiangsheng Shi +6
Binary Neural Networks (BNNs) have received significant attention due to their promising efficiency. Currently, most BNN studies directly adopt widely-used CNN architectures, which…
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator
Tong Wu, Xuefei Ning, Wenshuo Li +3
In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on ve…
Evaluating Efficient Performance Estimators of Neural Architectures
Xuefei Ning, Changcheng Tang, Wenshuo Li +4
Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS). To reduce the architecture training costs in NAS, one…
Exploring the Potential of Low-bit Training of Convolutional Neural Networks
Kai Zhong, Xuefei Ning, Guohao Dai +5
In this work, we propose a low-bit training framework for convolutional neural networks, which is built around a novel multi-level scaling (MLS) tensor format. Our framework focuse…
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
Xuefei Ning, Tianchen Zhao, Wenshuo Li +3
Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key pro…
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
Xuefei Ning, Yin Zheng, Tianchen Zhao +2
This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from ex…
Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud
Shulin Zeng, Guohao Dai, Hanbo Sun +5
FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based…
FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture
Xuefei Ning, Guangjun Ge, Wenshuo Li +6
With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (N…
Communication Lower Bound in Convolution Accelerators
Xiaoming Chen, Yinhe Han, Yu Wang
In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and meth…
Nonparametric Topic Modeling with Neural Inference
Xuefei Ning, Yin Zheng, Zhuxi Jiang +3
This work focuses on combining nonparametric topic models with Auto-Encoding Variational Bayes (AEVB). Specifically, we first propose iTM-VAE, where the topics are treated as train…
Hu-Fu: Hardware and Software Collaborative Attack Framework against Neural Networks
Wenshuo Li, Jincheng Yu, Xuefei Ning +4
Recently, Deep Learning (DL), especially Convolutional Neural Network (CNN), develops rapidly and is applied to many tasks, such as image classification, face recognition, image se…
A Survey of FPGA-Based Neural Network Accelerator
Kaiyuan Guo, Shulin Zeng, Jincheng Yu +2
Recent researches on neural network have shown significant advantage in machine learning over traditional algorithms based on handcrafted features and models. Neural network is now…
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao +2
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-…
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Song Han, Junlong Kang, Huizi Mao +9
Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built larger and larger models.…
NXgraph: An Efficient Graph Processing System on a Single Machine
Yuze Chi, Guohao Dai, Yu Wang +3
Recent studies show that graph processing systems on a single machine can achieve competitive performance compared with cluster-based graph processing systems. In this paper, we pr…
Network flow-based simultaneous retiming and slack budgeting for low power design
Bei Yu, Sheqin Dong, Yuchun Ma +4
Low power design has become one of the most significant requirements when CMOS technology entered the nanometer era. Therefore, timing budget is often performed to slow down as man…