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Logic synthesis tools face tough challenges when providing algorithms for synthesizing circuits with increased inputs and complexity. Machine learning techniques show high performance in solving specific problems, being an attractive option to improve electronic design tools. We explore Cartesian Genetic Programming (CGP) for logic optimization of exact or approximate Boolean functions in our work. The proposed CGP-based flow receives the expected circuit behavior as a truth-table and either performs the synthesis starting from random circuits or optimizes a circuit description provided in the format of an AND-Inverter Graph. The optimization flow improves solutions found by other techniques, using them for bootstrapping the evolutionary process. We use two metrics to evaluate our CGP-based flow: (i) the number of AIG nodes or (ii) the circuit accuracy. The results obtained showed that the CGP-based flow provided at least 22.6% superior results when considering the trade-off between accuracy and size compared with two other methods that brought the best accuracy and size outcomes, respectively.

This work evaluates the use of Decision Trees (DTs) methods for a fast logic minimization of Boolean functions. The proposed DT approach is compared to traditional Espresso logic minimizer and the minimization algorithms available in the ABC tool. The methods are compared with respect to the execution time, number of nodes and number of logic levels. The DT methods proved to be a faster alternative, reducing time by an average of 52% and 5.5% when compared to Espresso and ABC respectively, while keeping competitive results in terms of AIG depth and number of nodes. Additionally, in order to obtain smaller circuits at the cost of approximate results we tested DTs with limited tree depth. The trade-offs between synthesis time, circuit area and accuracy are also discussed. Compared to ABC, limiting the maximum tree depth leads to time savings of up to 52%, up to 86% less number of nodes, and up to 48% lower AIG depth, while maintaining acceptable accuracy results.

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