Graph-Guided Transfer Learning to Boost the Efficiency of System-Level Optimization of Analog/Mixed-Signal Circuits

This paper introduces a novel graph-guided transfer learning approach to boost the efficiency of system-level optimization of analog/mixed-signal circuits. The system-level optimization is based on Reinforcement Learning (RL) in combination with Graph Attention Networks (GAT). The results surpass state-of-the-art in efficiency and optimality. The key innovation is a graph similarity detection method that leverages embedded design knowledge to identify electrical similarities and trade-offs, enhancing knowledge transferability, even between dissimilar circuit architectures. Applied to the case study of 4th-order continuoustime Delta-Sigma analog-to-digital converters, the graph-based transfer learning framework enhances the RL sampling efficiency, reducing the amount of simulations by up to 11x, and improves the optimization results by 12.4% compared to optimization from scratch. As the framework accelerates knowledge transfer across different architectures, it can boost the optimization efficiency and improve the performance towards a broad range of analog/mixed-signal systems.