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Presentation

DeepGate: Learning Neural Representations of Logic Gates*
TimeWednesday, July 13th2:15pm - 2:37pm PDT
Location3000, Level 3
Event Type
Research Manuscript
Keywords
ML Algorithms and Applications
Topics
AI
DescriptionThe success of Deep Learning paved its path to EDA, where many DL solutions are proposed to solve specific problems. However, \textit{How to learn a good circuit representation?} is still an open question. In this work, we propose DeepGate, a neural representation learner for logic gates. It learns an effective representation by exploiting the various inductive biases from circuit structure as domain knowledge and uses logic simulated probabilities as a rich supervision for every node. The Experimental results show that DeepGate learns a reasonable representation from probability prediction and can be applied to downstream tasks like test point insertion problem.