30 citations · 62 across the 13 of their papers we have counts for
13 papers
DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations
Sadaf Khan, Zhengyuan Shi, Ziyang Zheng +2
Circuit representation learning is increasingly pivotal in Electronic Design Automation (EDA), serving various downstream tasks with enhanced model efficiency and accuracy. One not…
DeepGate3: Towards Scalable Circuit Representation Learning
Zhengyuan Shi, Ziyang Zheng, Sadaf Khan +3
Circuit representation learning has shown promising results in advancing the field of Electronic Design Automation (EDA). Existing models, such as DeepGate Family, primarily utiliz…
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…
Addressing Variable Dependency in GNN-based SAT Solving
Zhiyuan Yan, Min Li, Zhengyuan Shi +3
Boolean satisfiability problem (SAT) is fundamental to many applications. Existing works have used graph neural networks (GNNs) for (approximate) SAT solving. Typical GNN-based end…
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…
SATformer: Transformer-Based UNSAT Core Learning
Zhengyuan Shi, Min Li, Yi Liu +5
This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the…
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…
DeepSAT: An EDA-Driven Learning Framework for SAT
Min Li, Zhengyuan Shi, Qiuxia Lai +3
We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively we…
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…
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…
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,…
Skimming and Scanning for Untrimmed Video Action Recognition
Yunyan Hong, Ailing Zeng, Min Li +3
Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes. Untrimmed videos have redundant and diverse clips…
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…