DeepGate: Learning Neural Representations of Logic Gates
This addresses a foundational bottleneck in EDA for circuit designers, though it is an incremental step towards a broader goal.
The paper tackles the problem of obtaining a general neural representation of circuits in electronic design automation by proposing DeepGate, which embeds logic function and structural information as vectors on each gate, achieving effective signal probability prediction with strong generalization capability.
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 EDA problems. While demonstrating promising results, they require careful model tuning for every problem. The fundamental question on "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 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.