21 citations · 39 across the 10 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…
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…
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…