Sunday 02 February 2025
As technology continues to shrink, logic optimization in electronics design has become a critical challenge. Conventional methods rely on proxy metrics, which can lead to suboptimal designs. A team of researchers has developed an innovative approach that uses machine learning (ML) to predict post-mapping delay and area, enabling more effective logic optimization.
The traditional method involves optimizing designs using proxy metrics, such as node count and logical depth, to estimate final timing and area. However, these metrics often fail to accurately reflect the actual performance of a design after technology mapping and static timing analysis (STA). This can result in designs that are not optimized for delay and area.
To address this issue, the researchers developed an ML-enhanced logic optimization flow that directly incorporates exact post-mapping delay and area into the cost function. This approach avoids the need for costly STA runs during each iteration of the optimization process. Instead, the ML model predicts the post-mapping delay and area based on features extracted from the abstract representation of the circuit, known as an and-inverter graph (AIG).
The team trained their ML model using data from eight benchmark designs, each with different numbers of primary inputs and outputs. The model was evaluated against two other flows: a baseline flow that uses proxy metrics and a ground-truth-based flow that performs STA during each iteration.
The results showed that the ML-enhanced flow achieved nearly the same level of quality as the ground-truth-based flow, but with significantly reduced runtime. On average, the ML-enhanced flow was 80.83% faster than the ground-truth-based flow, and up to 88.79% faster in some cases.
The researchers also found that their approach outperformed the baseline flow, which relies on proxy metrics, by a significant margin. The Pareto-optimal front for the ML-enhanced flow was very close to that of the ground-truth-based flow, indicating that it can effectively explore the design space and identify better solutions.
This breakthrough has important implications for electronic design automation (EDA). As designs continue to grow in complexity, the need for efficient logic optimization methods becomes increasingly critical. The ML-enhanced approach offers a practical solution for large-scale designs, enabling designers to achieve better performance with reduced runtime overhead.
The researchers’ work demonstrates the potential of machine learning in addressing complex challenges in electronic design automation.
Cite this article: “Machine Learning-Based Logic Optimization for Efficient Electronic Design Automation”, The Science Archive, 2025.
Machine Learning, Electronic Design Automation, Logic Optimization, Post-Mapping Delay, Area Prediction, Technology Mapping, Static Timing Analysis, And-Inverter Graph, Benchmark Designs, Runtime Reduction
