Representation Learning of Logic Circuits

• URL: http://arxiv.org/abs/2111.14616v1
• Date: Fri, 26 Nov 2021 05:57:05 GMT
• Title: Representation Learning of Logic Circuits
• Authors: Min Li, Sadaf Khan, Zhengyuan Shi, Naixing Wang, Yu Huang, Qiang Xu
• Abstract summary: We propose a novel representation learning solution that embeds both logic function and structural information of a circuit as vectors on each gate. Specifically, we propose transforming circuits into unified and-inverter graph format for learning. We then introduce a novel graph neural network that uses strong inductive biases in practical circuits as learning priors for signal probability prediction.
• Score: 7.614021815435811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic in recent years. Most existing solutions apply well-developed DL models to solve specific EDA problems. While demonstrating promising results, they require careful model tuning for every problem. The fundamental question on \textit{"How to obtain a general and effective neural representation of circuits?"} has not been answered yet. In this work, we take the first step towards solving this problem. We propose \textit{DeepGate}, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate. Specifically, we propose transforming circuits into unified and-inverter graph format for learning and using signal probabilities as the supervision task in DeepGate. We then introduce a novel graph neural network that uses strong inductive biases in practical circuits as learning priors for signal probability prediction. Our experimental results show the efficacy and generalization capability of DeepGate.

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