AiLO: A Predictive Framework for Logic Optimization Using Multi-scale Cross-attention Transformer

doi:10.1145/3757319

AiLO: A Predictive Framework for Logic Optimization Using Multi-scale Cross-attention Transformer

2026-03-01

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DOI：10.1145/3757319

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Logic Optimization (LO) is a critical stage in the chip design process, focused on improving the Quality of Results (QoR) by optimizing circuit designs to minimize area and delay. During logic optimization, evaluating the QoR after each iteration requires completing logic optimization and technology mapping. The evaluation process is highly time-consuming, restricting the number of optimization iterations possible within a given time. To address this, the AI-aided logic optimization framework (AiLO) is developed to explore more optimization operator sequences (recipes). AiLO framework consists of two core components: AI-based metric evaluation and optimization exploration. To achieve accurate evaluation, different prediction models can be integrated. A multi-scale cross-attention Transformer (CrossLO) is introduced to simulate the optimization structure of recipes across circuit at various scales to enhance the prediction accuracy. Moreover, the AI evaluation module can effectively maintain the recipe ranking, even when prediction accuracy is biased. The logic optimization exploration algorithm integrated with CrossLO (AI evaluation) shows an average improvement of 14.75% over the initial version. NSGA-II (optimization module) integrated with CrossLO achieves a significant lead over other algorithms in the same time. In addition, the AiLO framework continues to grow with the performance of the two components, demonstrating strong adaptability and flexibility.

Keywords:

Logic synthesis

logic optimization

evaluation and optimization

graph neural network

transformer

AiLO: A Predictive Framework for Logic Optimization Using Multi-scale Cross-attention Transformer

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