13 citations · 17 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…