ReLS: Retrieval Is Efficient Knowledge Transfer For Logic Synthesis

Design houses manage extensive collections of legacy synthesis data containing valuable knowledge crucial for enhancing synthesis efficiency and performance. Effectively encapsulating and transferring this knowledge is a pressing research challenge. Previous methods incorporate synthesis knowledge into neural networks trained on legacy data, often necessitating retraining for updated databases and fine-tuning for new designs. This study introduces ReLS, a retrieval-powered logic synthesis framework that integrates network weight-captured knowledge with information stored in dynamic synthesis databases. ReLS employs a graph neural network-based And-Inverter Graphs (AIGs) encoder to convert new AIGs into vector representations for similarity searches against legacy AIGs. Top-ranked recipes from similar AIGs are retrieved and reranked to offer initial solutions for advanced synthesis optimization algorithms. ReLS serves as an initialization tool to streamline efficient knowledge transfer without the requirement for fine-tuning. Utilizing the OpenABC-D database with 61,500 synthesis samples and limiting to 50 synthesis runs for each design, ReLS enhances the quality-of-results improvements of various existing methods over handcrafted recipes by 8.6% to 196% on 10 unseen designs. Furthermore, experimental results showcase ReLS’s ability to harness databases not used during training.