247 citations · 762 across the 30 of their papers we have counts for
16 papers · 1 filter
FITS: Modeling Time Series with Parameters
Zhijian Xu, Ailing Zeng, Qiang Xu
In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the…
DeepGate2: Functionality-Aware Circuit Representation Learning
Zhengyuan Shi, Hongyang Pan, Sadaf Khan +7
Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and l…
FrAug: Frequency Domain Augmentation for Time Series Forecasting
Muxi Chen, Zhijian Xu, Ailing Zeng +1
Data augmentation (DA) has become a de facto solution to expand training data size for deep learning. With the proliferation of deep models for time series analysis, various time s…
DeepSeq: Deep Sequential Circuit Learning
Sadaf Khan, Zhengyuan Shi, Min Li +1
Circuit representation learning is a promising research direction in the electronic design automation (EDA) field. With sufficient data for pre-training, the learned general yet ef…
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning
Zhiyuan He, Yijun Yang, Pin-Yu Chen +2
Deep Neural Networks (DNNs) have achieved excellent performance in various fields. However, DNNs' vulnerability to Adversarial Examples (AE) hinders their deployments to safety-cri…
DeepTPI: Test Point Insertion with Deep Reinforcement Learning
Zhengyuan Shi, Min Li, Sadaf Khan +4
Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this…
DeepFIB: Self-Imputation for Time Series Anomaly Detection
Minhao Liu, Zhijian Xu, Qiang Xu
Time series (TS) anomaly detection (AD) plays an essential role in various applications, e.g., fraud detection in finance and healthcare monitoring. Due to the inherently unpredict…
Testability-Aware Low Power Controller Design with Evolutionary Learning
Min Li, Zhengyuan Shi, Zezhong Wang +3
XORNet-based low power controller is a popular technique to reduce circuit transitions in scan-based testing. However, existing solutions construct the XORNet evenly for scan chain…
DeepGate: Learning Neural Representations of Logic Gates
Min Li, Sadaf Khan, Zhengyuan Shi +3
Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic. Most solutions apply well-developed DL models to solve specific…
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
Minhao Liu, Ailing Zeng, Muxi Chen +4
One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose…
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
Yu Li, Min Li, Qiuxia Lai +2
Deep learning (DL) has achieved unprecedented success in a variety of tasks. However, DL systems are notoriously difficult to test and debug due to the lack of explainability of DL…
Relational Graph Neural Network Design via Progressive Neural Architecture Search
Ailing Zeng, Minhao Liu, Zhiwei Liu +3
We propose a novel solution to addressing a long-standing dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful in…
AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference
Min Li, Yu Li, Ye Tian +2
This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions. For a given input,…
DeepDyve: Dynamic Verification for Deep Neural Networks
Yu Li, Min Li, Bo Luo +2
Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, how…
Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples
Bo Luo, Qiang Xu
Deep neural networks (DNNs) are shown to be susceptible to adversarial example attacks. Most existing works achieve this malicious objective by crafting subtle pixel-wise perturbat…
On Functional Test Generation for Deep Neural Network IPs
Bo Luo, Yu Li, Lingxiao Wei +1
Machine learning systems based on deep neural networks (DNNs) produce state-of-the-art results in many applications. Considering the large amount of training data and know-how requ…